{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 集成基本思想："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练时用多种分类器一起完成同一份任务\n",
    "![title](./img/1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试时对待测试样本分别通过不同的分类器，汇总最后的结果\n",
    "![title](./img/2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\envs\\sklearn_env\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:17: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
      "  from collections import Mapping, defaultdict\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 2) (500,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25557f6b248>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'yo', alpha=0.6)\n",
    "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'bs', alpha=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 投票策略：软投票与硬投票\n",
    "- 硬投票：直接用类别值，少数服从多数\n",
    "- 软投票：各自分类器的概率值进行加权平均"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 硬投票实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\envs\\sklearn_env\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(random_state=42)\n",
    "\n",
    "voting_clf = VotingClassifier(estimators=[('log', log_clf), ('rf', rnd_clf),\n",
    "                                          ('svc', svm_clf)],\n",
    "                              voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('log', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)), ('rf', RandomFo...f',\n",
       "  max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.896\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\envs\\sklearn_env\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 软投票实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "loglog_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(probability=True, random_state=42)  # 软投票：要求必须各个分别器都能得出概率值\n",
    "\n",
    "voting_clf = VotingClassifier(estimators=[('log', log_clf), ('rf', rnd_clf),\n",
    "                                          ('svc', svm_clf)],\n",
    "                              voting='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('log', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)), ('rf', RandomFo...bf',\n",
       "  max_iter=-1, probability=True, random_state=42, shrinking=True,\n",
       "  tol=0.001, verbose=False))],\n",
       "         flatten_transform=None, n_jobs=1, voting='soft', weights=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.912\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\envs\\sklearn_env\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging策略\n",
    "- 首先对训练数据集进行多次采样，保证每次得到的采样数据都是不同的\n",
    "- 分别训练多个模型，例如树模型\n",
    "- 预测时需得到所有模型结果再进行集成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(),\n",
    "    n_estimators=500,\n",
    "    max_samples=100,  # 每个基本模型使用的样本\n",
    "    bootstrap=True,  # 有放回抽样\n",
    "    n_jobs=-1,  # 并行使用所有CPU\n",
    "    random_state=42)\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.904"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.856"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 决策边界\n",
    "- 集成与传统方法对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "def plot_decision_boundary(clf,\n",
    "                           X,\n",
    "                           y,\n",
    "                           axes=[-2, 3, -2, 2],\n",
    "                           alpha=0.6,\n",
    "                           contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    coustom_comp = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, cmap=coustom_comp, alpha=0.3)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58', '#4c4c7f', '#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=coustom_comp, alpha=1)\n",
    "    plt.plot(X[:, 0][y == 0], X[:, 1][y == 1], 'yo', alpha=0.6)\n",
    "    plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'bs', alpha=0.6)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel('x1')\n",
    "    plt.ylabel('x2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Tree with Bagging')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title('Decision Tree')\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title('Decision Tree with Bagging')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Tree with Bagging')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y, contour=True)\n",
    "plt.title('Decision Tree')\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y, contour=True)\n",
    "plt.title('Decision Tree with Bagging')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colormap颜色：https://blog.csdn.net/zhaogeng111/article/details/78419015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OOB策略\n",
    "- Out Of Bag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=100, n_estimators=500, n_jobs=-1, oob_score=True,\n",
       "         random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(),\n",
    "    n_estimators=500,\n",
    "    max_samples=100,  # 每个基本模型使用的样本\n",
    "    bootstrap=True,  # 有放回抽样\n",
    "    n_jobs=-1,  # 并行使用所有CPU\n",
    "    random_state=42,\n",
    "    oob_score=True)\n",
    "bag_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9253333333333333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.904"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = bag_clf.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.35849057, 0.64150943],\n",
       "       [0.43513514, 0.56486486],\n",
       "       [1.        , 0.        ],\n",
       "       [0.0128866 , 0.9871134 ],\n",
       "       [0.03174603, 0.96825397],\n",
       "       [0.07928389, 0.92071611],\n",
       "       [0.4027027 , 0.5972973 ],\n",
       "       [0.06703911, 0.93296089],\n",
       "       [0.92950392, 0.07049608],\n",
       "       [0.88461538, 0.11538462],\n",
       "       [0.59351621, 0.40648379],\n",
       "       [0.04896907, 0.95103093],\n",
       "       [0.7591623 , 0.2408377 ],\n",
       "       [0.82908163, 0.17091837],\n",
       "       [0.88279302, 0.11720698],\n",
       "       [0.07407407, 0.92592593],\n",
       "       [0.04488778, 0.95511222],\n",
       "       [0.92307692, 0.07692308],\n",
       "       [0.70737913, 0.29262087],\n",
       "       [0.94358974, 0.05641026],\n",
       "       [0.06366048, 0.93633952],\n",
       "       [0.22933333, 0.77066667],\n",
       "       [0.91002571, 0.08997429],\n",
       "       [0.98746867, 0.01253133],\n",
       "       [0.96236559, 0.03763441],\n",
       "       [0.        , 1.        ],\n",
       "       [0.94255875, 0.05744125],\n",
       "       [1.        , 0.        ],\n",
       "       [0.02933333, 0.97066667],\n",
       "       [0.70454545, 0.29545455],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01262626, 0.98737374],\n",
       "       [0.0880829 , 0.9119171 ],\n",
       "       [0.09090909, 0.90909091],\n",
       "       [0.9762533 , 0.0237467 ],\n",
       "       [0.01827676, 0.98172324],\n",
       "       [0.54255319, 0.45744681],\n",
       "       [0.02122016, 0.97877984],\n",
       "       [0.98979592, 0.01020408],\n",
       "       [0.10242588, 0.89757412],\n",
       "       [0.34564644, 0.65435356],\n",
       "       [0.98684211, 0.01315789],\n",
       "       [0.98714653, 0.01285347],\n",
       "       [0.00755668, 0.99244332],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.0596206 , 0.9403794 ],\n",
       "       [0.97727273, 0.02272727],\n",
       "       [0.05691057, 0.94308943],\n",
       "       [0.94177215, 0.05822785],\n",
       "       [0.7847769 , 0.2152231 ],\n",
       "       [0.91948052, 0.08051948],\n",
       "       [0.80738786, 0.19261214],\n",
       "       [0.01758794, 0.98241206],\n",
       "       [0.0874036 , 0.9125964 ],\n",
       "       [0.77197802, 0.22802198],\n",
       "       [0.02439024, 0.97560976],\n",
       "       [0.01612903, 0.98387097],\n",
       "       [0.0199005 , 0.9800995 ],\n",
       "       [0.81395349, 0.18604651],\n",
       "       [0.6716792 , 0.3283208 ],\n",
       "       [0.72176309, 0.27823691],\n",
       "       [0.9921875 , 0.0078125 ],\n",
       "       [0.01049869, 0.98950131],\n",
       "       [0.75668449, 0.24331551],\n",
       "       [0.97727273, 0.02272727],\n",
       "       [0.99230769, 0.00769231],\n",
       "       [0.60629921, 0.39370079],\n",
       "       [0.98461538, 0.01538462],\n",
       "       [0.3556701 , 0.6443299 ],\n",
       "       [0.30666667, 0.69333333],\n",
       "       [0.41891892, 0.58108108],\n",
       "       [0.72386059, 0.27613941],\n",
       "       [0.        , 1.        ],\n",
       "       [0.25789474, 0.74210526],\n",
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       "       [1.        , 0.        ],\n",
       "       [0.02770781, 0.97229219],\n",
       "       [0.96363636, 0.03636364],\n",
       "       [0.00512821, 0.99487179],\n",
       "       [0.18181818, 0.81818182],\n",
       "       [0.13299233, 0.86700767],\n",
       "       [0.40782123, 0.59217877],\n",
       "       [0.98704663, 0.01295337],\n",
       "       [0.04123711, 0.95876289],\n",
       "       [0.66758242, 0.33241758],\n",
       "       [0.07341772, 0.92658228],\n",
       "       [0.01315789, 0.98684211],\n",
       "       [0.        , 1.        ],\n",
       "       [0.38046272, 0.61953728],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01503759, 0.98496241],\n",
       "       [0.05277045, 0.94722955],\n",
       "       [0.01542416, 0.98457584],\n",
       "       [0.81914894, 0.18085106],\n",
       "       [0.71967655, 0.28032345],\n",
       "       [0.07936508, 0.92063492],\n",
       "       [1.        , 0.        ],\n",
       "       [0.35526316, 0.64473684],\n",
       "       [0.67195767, 0.32804233],\n",
       "       [0.01542416, 0.98457584],\n",
       "       [0.12533333, 0.87466667],\n",
       "       [0.42746114, 0.57253886],\n",
       "       [0.96623377, 0.03376623],\n",
       "       [0.04178273, 0.95821727],\n",
       "       [0.97243108, 0.02756892],\n",
       "       [0.44235925, 0.55764075],\n",
       "       [0.28759894, 0.71240106],\n",
       "       [0.9974026 , 0.0025974 ],\n",
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       "       [0.26835443, 0.73164557],\n",
       "       [0.7849162 , 0.2150838 ],\n",
       "       [0.9893617 , 0.0106383 ],\n",
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       "       [0.4859335 , 0.5140665 ],\n",
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       "       [0.9921466 , 0.0078534 ],\n",
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       "       [1.        , 0.        ],\n",
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       "       [0.03580563, 0.96419437],\n",
       "       [0.95511222, 0.04488778],\n",
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       "       [0.02624672, 0.97375328],\n",
       "       [0.23577236, 0.76422764],\n",
       "       [0.8630491 , 0.1369509 ],\n",
       "       [0.4119171 , 0.5880829 ],\n",
       "       [0.92207792, 0.07792208],\n",
       "       [0.002457  , 0.997543  ],\n",
       "       [0.0265252 , 0.9734748 ],\n",
       "       [0.82849604, 0.17150396],\n",
       "       [0.76863753, 0.23136247],\n",
       "       [0.54427083, 0.45572917],\n",
       "       [0.88413098, 0.11586902],\n",
       "       [0.94329897, 0.05670103],\n",
       "       [0.11979167, 0.88020833],\n",
       "       [0.78205128, 0.21794872],\n",
       "       [0.08136483, 0.91863517],\n",
       "       [0.01025641, 0.98974359],\n",
       "       [0.12532637, 0.87467363],\n",
       "       [0.73969072, 0.26030928],\n",
       "       [0.96946565, 0.03053435],\n",
       "       [1.        , 0.        ],\n",
       "       [0.03403141, 0.96596859],\n",
       "       [0.00265957, 0.99734043],\n",
       "       [0.05943152, 0.94056848],\n",
       "       [0.02842377, 0.97157623],\n",
       "       [0.9924812 , 0.0075188 ],\n",
       "       [0.98102981, 0.01897019],\n",
       "       [0.86720867, 0.13279133],\n",
       "       [0.99730458, 0.00269542],\n",
       "       [1.        , 0.        ],\n",
       "       [0.87335092, 0.12664908],\n",
       "       [0.0129199 , 0.9870801 ],\n",
       "       [0.64925373, 0.35074627],\n",
       "       [0.33756345, 0.66243655],\n",
       "       [0.07336957, 0.92663043],\n",
       "       [0.01534527, 0.98465473],\n",
       "       [0.37922078, 0.62077922],\n",
       "       [0.99728997, 0.00271003],\n",
       "       [0.97282609, 0.02717391],\n",
       "       [0.        , 1.        ],\n",
       "       [0.9973545 , 0.0026455 ],\n",
       "       [0.07297297, 0.92702703],\n",
       "       [0.00520833, 0.99479167],\n",
       "       [0.92021277, 0.07978723],\n",
       "       [0.02077922, 0.97922078],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.04603581, 0.95396419],\n",
       "       [0.81725888, 0.18274112],\n",
       "       [0.9       , 0.1       ],\n",
       "       [0.033241  , 0.966759  ],\n",
       "       [0.94818653, 0.05181347],\n",
       "       [0.9071618 , 0.0928382 ],\n",
       "       [0.96373057, 0.03626943],\n",
       "       [0.01312336, 0.98687664],\n",
       "       [0.01591512, 0.98408488],\n",
       "       [0.99212598, 0.00787402],\n",
       "       [0.24427481, 0.75572519],\n",
       "       [0.98958333, 0.01041667],\n",
       "       [0.1344086 , 0.8655914 ],\n",
       "       [0.01808786, 0.98191214],\n",
       "       [0.98969072, 0.01030928],\n",
       "       [0.        , 1.        ],\n",
       "       [0.21038251, 0.78961749],\n",
       "       [0.89238845, 0.10761155],\n",
       "       [0.91122715, 0.08877285],\n",
       "       [0.62005277, 0.37994723],\n",
       "       [0.68533333, 0.31466667],\n",
       "       [0.04336735, 0.95663265],\n",
       "       [0.24479167, 0.75520833],\n",
       "       [0.98666667, 0.01333333],\n",
       "       [0.92875989, 0.07124011],\n",
       "       [0.92245989, 0.07754011],\n",
       "       [0.98387097, 0.01612903],\n",
       "       [0.03968254, 0.96031746],\n",
       "       [0.01302083, 0.98697917],\n",
       "       [0.0971867 , 0.9028133 ],\n",
       "       [0.51785714, 0.48214286],\n",
       "       [0.        , 1.        ],\n",
       "       [0.02046036, 0.97953964],\n",
       "       [0.97474747, 0.02525253],\n",
       "       [0.09189189, 0.90810811],\n",
       "       [0.12403101, 0.87596899],\n",
       "       [0.88804071, 0.11195929],\n",
       "       [0.0536193 , 0.9463807 ],\n",
       "       [0.37317073, 0.62682927],\n",
       "       [0.01084011, 0.98915989],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01302083, 0.98697917],\n",
       "       [0.01369863, 0.98630137],\n",
       "       [0.91052632, 0.08947368],\n",
       "       [0.9012987 , 0.0987013 ],\n",
       "       [0.95641026, 0.04358974],\n",
       "       [0.02150538, 0.97849462],\n",
       "       [0.05927835, 0.94072165],\n",
       "       [0.95989305, 0.04010695],\n",
       "       [0.1193634 , 0.8806366 ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.23218997, 0.76781003],\n",
       "       [0.97066667, 0.02933333],\n",
       "       [0.84594595, 0.15405405],\n",
       "       [0.12207792, 0.87792208],\n",
       "       [0.71891892, 0.28108108],\n",
       "       [0.93350384, 0.06649616],\n",
       "       [0.14516129, 0.85483871],\n",
       "       [0.13178295, 0.86821705],\n",
       "       [0.98727735, 0.01272265],\n",
       "       [0.        , 1.        ],\n",
       "       [0.01358696, 0.98641304],\n",
       "       [0.01842105, 0.98157895],\n",
       "       [0.38324873, 0.61675127],\n",
       "       [0.86315789, 0.13684211],\n",
       "       [0.04884319, 0.95115681],\n",
       "       [0.9893617 , 0.0106383 ],\n",
       "       [0.84679666, 0.15320334],\n",
       "       [0.0025641 , 0.9974359 ],\n",
       "       [0.76363636, 0.23636364],\n",
       "       [0.98737374, 0.01262626],\n",
       "       [0.00791557, 0.99208443],\n",
       "       [0.98971722, 0.01028278],\n",
       "       [0.05913978, 0.94086022],\n",
       "       [0.01305483, 0.98694517],\n",
       "       [0.11653117, 0.88346883],\n",
       "       [0.25065963, 0.74934037],\n",
       "       [0.89312977, 0.10687023],\n",
       "       [0.05912596, 0.94087404],\n",
       "       [0.98694517, 0.01305483],\n",
       "       [0.60349127, 0.39650873],\n",
       "       [0.08080808, 0.91919192],\n",
       "       [0.632     , 0.368     ],\n",
       "       [0.88688946, 0.11311054],\n",
       "       [0.00787402, 0.99212598],\n",
       "       [0.99238579, 0.00761421],\n",
       "       [0.01041667, 0.98958333],\n",
       "       [0.        , 1.        ],\n",
       "       [0.76616915, 0.23383085],\n",
       "       [0.        , 1.        ],\n",
       "       [0.98918919, 0.01081081],\n",
       "       [0.1038961 , 0.8961039 ],\n",
       "       [0.73589744, 0.26410256],\n",
       "       [0.14054054, 0.85945946],\n",
       "       [0.9972973 , 0.0027027 ],\n",
       "       [0.89322917, 0.10677083],\n",
       "       [0.01028278, 0.98971722],\n",
       "       [0.06332454, 0.93667546],\n",
       "       [0.12827225, 0.87172775],\n",
       "       [0.08967391, 0.91032609],\n",
       "       [0.        , 1.        ],\n",
       "       [0.96899225, 0.03100775],\n",
       "       [0.84615385, 0.15384615],\n",
       "       [0.15549598, 0.84450402],\n",
       "       [0.93129771, 0.06870229],\n",
       "       [0.04221636, 0.95778364],\n",
       "       [0.6278481 , 0.3721519 ],\n",
       "       [0.1443299 , 0.8556701 ],\n",
       "       [0.95064935, 0.04935065],\n",
       "       [0.90858726, 0.09141274],\n",
       "       [0.00789474, 0.99210526],\n",
       "       [0.94559585, 0.05440415],\n",
       "       [0.90909091, 0.09090909],\n",
       "       [0.        , 1.        ],\n",
       "       [0.05319149, 0.94680851],\n",
       "       [1.        , 0.        ],\n",
       "       [0.02917772, 0.97082228],\n",
       "       [0.98963731, 0.01036269],\n",
       "       [0.09459459, 0.90540541],\n",
       "       [0.89304813, 0.10695187],\n",
       "       [1.        , 0.        ],\n",
       "       [0.01333333, 0.98666667],\n",
       "       [0.05121294, 0.94878706],\n",
       "       [0.68      , 0.32      ],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.68717949, 0.31282051],\n",
       "       [0.87212276, 0.12787724],\n",
       "       [0.98974359, 0.01025641],\n",
       "       [0.67539267, 0.32460733],\n",
       "       [0.49333333, 0.50666667],\n",
       "       [0.01362398, 0.98637602],\n",
       "       [0.83037975, 0.16962025],\n",
       "       [0.01591512, 0.98408488],\n",
       "       [1.        , 0.        ],\n",
       "       [0.78042328, 0.21957672],\n",
       "       [0.9871134 , 0.0128866 ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.85025381, 0.14974619],\n",
       "       [0.27979275, 0.72020725],\n",
       "       [0.1689008 , 0.8310992 ],\n",
       "       [0.2382199 , 0.7617801 ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.75590551, 0.24409449],\n",
       "       [0.9038961 , 0.0961039 ],\n",
       "       [0.05882353, 0.94117647],\n",
       "       [1.        , 0.        ],\n",
       "       [0.97567568, 0.02432432],\n",
       "       [0.98992443, 0.01007557],\n",
       "       [0.00507614, 0.99492386],\n",
       "       [0.07653061, 0.92346939],\n",
       "       [0.91794872, 0.08205128],\n",
       "       [0.93523316, 0.06476684],\n",
       "       [0.9973822 , 0.0026178 ],\n",
       "       [0.24378109, 0.75621891],\n",
       "       [0.992     , 0.008     ],\n",
       "       [0.135     , 0.865     ],\n",
       "       [0.94845361, 0.05154639],\n",
       "       [0.04773869, 0.95226131],\n",
       "       [0.98777506, 0.01222494],\n",
       "       [0.99479167, 0.00520833],\n",
       "       [0.98271605, 0.01728395],\n",
       "       [0.        , 1.        ],\n",
       "       [0.93882979, 0.06117021],\n",
       "       [0.01591512, 0.98408488],\n",
       "       [0.06958763, 0.93041237],\n",
       "       [0.06127451, 0.93872549],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.98641304, 0.01358696],\n",
       "       [0.        , 1.        ],\n",
       "       [0.95989305, 0.04010695],\n",
       "       [0.07754011, 0.92245989],\n",
       "       [0.98469388, 0.01530612],\n",
       "       [0.18489583, 0.81510417],\n",
       "       [0.0156658 , 0.9843342 ],\n",
       "       [0.04569892, 0.95430108],\n",
       "       [0.        , 1.        ],\n",
       "       [0.81967213, 0.18032787],\n",
       "       [0.08521303, 0.91478697],\n",
       "       [0.13456464, 0.86543536],\n",
       "       [1.        , 0.        ],\n",
       "       [0.93229167, 0.06770833],\n",
       "       [0.23015873, 0.76984127],\n",
       "       [0.94191919, 0.05808081],\n",
       "       [0.05093834, 0.94906166],\n",
       "       [0.12834225, 0.87165775],\n",
       "       [1.        , 0.        ],\n",
       "       [0.91374663, 0.08625337],\n",
       "       [0.60638298, 0.39361702],\n",
       "       [0.87399464, 0.12600536],\n",
       "       [1.        , 0.        ],\n",
       "       [0.02419355, 0.97580645],\n",
       "       [0.94933333, 0.05066667],\n",
       "       [0.03523035, 0.96476965],\n",
       "       [0.13612565, 0.86387435],\n",
       "       [0.9093199 , 0.0906801 ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.08854167, 0.91145833],\n",
       "       [0.69086022, 0.30913978]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(375, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_clf.oob_decision_function_.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_clf = RandomForestClassifier()\n",
    "rf_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征重要性：\n",
    "sklearn中是看每个特征的平均深度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) 0.11105536416721994\n",
      "sepal width (cm) 0.02319505364393038\n",
      "petal length (cm) 0.44036215067701534\n",
      "petal width (cm) 0.42538743151183406\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "rf_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1)\n",
    "rf_clf.fit(iris['data'], iris['target'])\n",
    "for name, score in zip(iris['feature_names'],\n",
    "                       rf_clf.feature_importances_):  # 重要性不排序\n",
    "    print(name, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "# 如果无法导入，是版本问题，将scikit-learn版本降为0.19就可以了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1)\n",
    "rf_clf.fit(mnist['data'], mnist['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf.feature_importances_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1.87874489e-06, 2.60914946e-07, 1.84017419e-07, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.17580073e-08,\n",
       "       4.48108160e-06, 8.85851286e-06, 2.01366768e-05, 3.51586631e-05,\n",
       "       7.41815441e-05, 1.24800010e-04, 1.36747694e-04, 1.50336757e-04,\n",
       "       2.59419220e-04, 2.78608226e-04, 2.76178854e-04, 1.71693590e-04,\n",
       "       1.77798872e-04, 1.08070601e-04, 9.01946572e-05, 4.20652789e-05,\n",
       "       2.30011871e-05, 1.26379651e-05, 3.43844849e-06, 1.25563050e-06,\n",
       "       3.06685909e-07, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       4.76373233e-08, 0.00000000e+00, 1.81166194e-07, 3.43855429e-07,\n",
       "       1.25914185e-06, 2.74720236e-06, 3.03669902e-06, 2.73916906e-06,\n",
       "       3.32066919e-06, 3.62969503e-06, 7.03438260e-06, 3.45580551e-06,\n",
       "       2.46809487e-06, 4.24498393e-06, 2.20406653e-06, 1.68064984e-06,\n",
       "       1.44982031e-06, 8.85475312e-07, 1.10642346e-06, 6.50829009e-07,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28, 28)\n",
    "    plt.imshow(image, cmap=matplotlib.cm.hot)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1, 0, 'Not important'), Text(1, 0, 'Very important')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_digit(rf_clf.feature_importances_)\n",
    "char = plt.colorbar(ticks=[\n",
    "    rf_clf.feature_importances_.min(),\n",
    "    rf_clf.feature_importances_.max()\n",
    "])\n",
    "char.ax.set_yticklabels(['Not important', 'Very important'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting-提升策略\n",
    "AdaBoost\n",
    "跟上学时的考试一样，这次做错的题，是不是得额外注意，下次的时候就和别错了！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/4.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B+PDIHNz1ryNuAgyIDrMYsDSnlFKDRez96VSMrWJGYxGvbrwbHtyBVVQHBqz8bCrkpXydqOIz4E+BzwEF3ZTdZ4y5t/+rpJRS7TkkKDNxTp+cyjtVIdz1mxDLYFUOJ9ro4i5/FwwkTs7GVJzEDsTBs4hYhdqoGtg0p5RSg8LquZcIXxvDc/vmIA9vwypo9KdStwq1UdUDedmoMsa8CCAiy4DxOa6OUkp1yMGjnDjvvDOb92IJAve/7Y8CPDGX+KTz2KU3seM20X3Lce7djwg4xqXQKtAG1QCnOaWUGng6Hnou10awYe98Ap/fgNgGy7OIWBHNqR4aDM3PxSJyQ0Q+FJHviUheNhSVUoNPAIMAFy+WY024CMD0aDHRE/OxiutAYMyhu5BIg9+gwiEseoZqCNKcUkrlmAfU+ddLtXH9einWyEq/QYU2qHproDeqdgHzgVHAF4HfBL7ZUUER+Wpy/PvhGzduZbGKSqmhpNC7833ZAqxE6v2BfshVvaA5pZTKsRiJRAPbdyzu5FzVHRaWNqh6aUAnvDHmrDHmE2OMZ4w5AfwQ+LVOyv7UGLPMGLNsxIhh2a2oUmpQcrqNJzXUaU4ppXLLAxo5dGgOnxbWgKW51V8GdKOqAwa0ea2Uyg5LG1Wq5zSnlFJZ5I/3q60NI5HbOa7L4JaXjSoRcUQkBNiALSKhjsagi8g6ESlP/jwb+B7wSnZrq5RSaqjRnFJKKZUqLxtVwHeBBuDbwO8kf/6uiExMrvExMVnuIeC4iNQBG4EXgR/losJKKdWWMcLZ6iD23FOAXlM1yGhOKaUGlAfHV+HFbHbsWohzzx7EAgx8cG4U9pJ3MbouVZ/k5QxExpgfAD/o5OFISrlvAN/IQpWUUqrHPAP2Y1uSU6k7BCSQ6yqpDNGcUkoNJPfff4j6+kKe37CExJqdOMNuYzxInJuIvW4rYhnEWIQI5bqqA1ZeNqqUUmpgaH9NlfEEbH8Mu1gGDIQIEZRgtiunlFJqyEsAcONGKVsOzcF5YhOWk4CEReKTiTjTz2EMBAkSlKDO/NcHeo5PKaV6oZA4oUT77cc+HEdg/euYZHsrLGGCaINKKaVUtsXwvBgAp05NRaafQZwEjoHoL34Te+p5AMJSSIiQNqj6SBtVSinVI4bhNGHdCnHo2PR2j8ri41iF0ZaTWI4OCFBKKZVVBohSXw9nz4/ytxihOZjKPBc8p+W+5lRmaKNKKaV6YAQx4tWFvPzGXYQWnGz3uAjEjs3zLwBWSimlsi5KNCq88PL9JMZfynVlhgyNfaWU6oEAhsrKEiipIdJB5155LETivXnZr5hSSikFQILq6iLiwSbE9XJdmSFDG1VKKZVBAc/OdRWUUkoplWXaqFJKqV6wLY+R2FRWFlHjecjISgCqb0WwZ5zJce2UUkoNdYKhjPYdfXX1IawpZ3WYeobplWlKKdVDjhPji/cd4/z70zhwJYL75OuIbTAGbjpR3OVHwECBFOS6qkoppYYgy/J4cu1BTNUwKK1t9ViNxAk8sBsMOo16BmkbVSmlemjs2Bu8/fYiDll1BB7Yi1iGRE0YEbDKqsETwhImgC72q5RSKvtKS2/x/nvT+bi+/ZkqK9IARihMTqWuMkMbVUoplRZ/KnU8+OyzUVxocHFmnQEDgbfvhppiAASh2CrSKWqVUkplmT+VuufBrVslXK0pYOn4W5w9O5ZLJo498+OWYkUSwcXNaW0HG21UKaVUGspp4srHYzBGiMdtxIkDUCIWzvVRLeVccXQohVJKqSwzQD2XLpXQ0OjiulE+//Ah9uxexO46cNZuR5wExoArLpY2ATJOP1GllOpGAQmOHZ7NnhsFYLWZntZAImGDk8hN5ZRSSg1xHlDH++9P4s0PxhIMNVJYGOOVV1dzfsYp3IpTACRuDPOHqevX/36hn6pSSnVDgFOnpmLPP0Xbk1C3awuoW/IO9uhrGKNhpZRSKtv8kRMnTkzHrjiFLUJlZQnVCcEefR2AksNLMNdGdbUT1Uea/koplZaOh/QlCutxppzHJER7AJVSSuUJaRVboeqy3FVliND0V0qpbsRjNgmnCQnE2h00xTaYqmGY2nBO6qaUUmooM4BHY6NLYwJCBfUdFqmpLUCGV2e9dkOJTk+llFLdOH91OIFnXkLcuJ9fKYwHJtCYnKIWHNHDqlJKqWzwJ6e4dq2EN/bPYdQXNvCAG8B47UdW1K/eiV1aDcmJKlTm6ZkqpZTqhoy7jLhxykwQpE1Yib/mh3gWxVKsw/+UUkplQQJj6jh2bBobPxzFgie2sdpE2LDxnnaj1Q1glVaDZ1EkRdi0X7tK9Z2mv1JKtWLabRFgKkUskgjxuMUHH43HmvGR/5hAwAQosiI6lbpSSqksMEADFy6Uc+TTkUy79ygLrQLe2rqS8NKjSJscEwHHuBRbEe3460c6TkUppVpp36gKisVcbEx1kBd2LCF+/06cYbcxxg+rAinIQT2VUkoNZfX1BUi4nsJkh97UqReYXJIgGg20KxuWwmxXb8jR5qpSSnVjFDZV50bz8r65JB7fhDXsNl5tuN1IQKWUUirbmr/Ml5XV8PzWpZhgY07rM1Rpo0oppVoYINpuqwPs2bMY+579/or0l0aDrvWrlFIq6/yhf5WVRbzzwViclQcpNf41Unv2LGTpA/sx0RC79s/FWb0rt1UdYnT4n1JKARDHmCjHT0xh4YJP2j3qeRaWlWxJlV/FcgzGEyKWDqlQSimVDQmggVOnJnPwWhh3/SYsyxD0IiCwZs1hGqpH8MKxKdiPvoEdjGE8ED2FkhX6MSulFDGMifLGG3fzcUll98Vtgxih2IrgaN+UUkqpfuc3qHbvXsihWJzAmr2IGGaZCCMtf4r0q1dHsvWjcuzHtiDBGIlLo7VBlUX6USulFDEaGoJ8dqUMe9LFbks7Yuv06UoppbKoCYCPPpqEM+c0AMtlGAukoGU4+smT07Dnv48IeFdGYI28AaBZlSV52cUqIl8HvgxUAP9qjPlyF2X/I/AtoAB4Afh9Y4xeodeFEye+Q03NQfxxuUJx8QoqKp7t9jGlBrfWs06sWjUTYwDm3tn4lWfAMvxr9Fc92vPToc9DB4sxYhleir7a86qqnNOc6l+aU0p1zQHmh9Z3sNDv42AZ/rHhFy1TqRemMUOt5lTf5WvT9TPgT4F/6KqQiHwO+DbwEDAZmAr8cX9XbiDzw+gAd6aNNtTUHODEie90+dhA9MMf/hOBwNpWtwkTnsl1tVTeMYDHzZsRpKSaEiw8D3+6dMvcuYl/v8PQ6Y4nrfeVvPVqXypfaE71k6GUU0qlx8+pW7fCEK4FO8GjFGE8QQQsy2C1ySkBCiggLIXpraGoOdVneXmmyhjzIoCILAPGd1H03wN/b4x5L1n+T4Bf4geY6oDfuwete+VNyvbuHuu7bPYyzpw5ga1bf9xy37bztR9B5UYCYxo4fnw6R29bLHlyCzOjw3ll6wqA9j2Axr//m6Ff1567IU5zqv8MtZxSqmv+tVQffTSBvedLCf7ay1i2oRh/xj9jwJiUfw/Jn79c8CXNqSwb6N8w5wHHUu4fA8pFpCxH9RkA2i9semd7V49lRrZ7GR3HZvTo0pbbyJHD+uV11EDURCzWxKYtKzlVcpXH7n2P8mvjeG7LUurWbu36qdpzp9KnOdVjQyunlOpcjEQiyvYdi9kXi+E+tBOxPKaaNGad1ZzKuoHeqIoA1Sn3m38ualtQRL4qIodF5PCNG7eyUrn81Nk/Munmscxo3QN55zUz3cvY7JNPLjN58m8wc+bv8Nu//aecPftZv7yOGmgSQBNvv13BjdEXuXvyDYY1hdj8xl3Yj27FCunlLipjNKd6bGjllFId84BGjh+fwaduA87c0whwt5SyRAqT1/yqfDLQG1W1QHHK/eafb7ctaIz5qTFmmTFm2YgRQ/dsRXHxiuRPhtRev+LiFV0+ljn938vYbMWKOfyv//VNXn31z/ibv/mPXL1axf33/99UVlZ3/2Q1yPl/b/X1BRCupxCLeNzFMxYSiOe4bmqQ0ZzqoaGUU0p1rjmnQkhhAwAzJMxEoKYyhOiJqLwz0BtV7wELU+4vBK4aY9JYaGZoqqh4luLildzp1ROKi1dSUfFsl49lTv/3MjZ79NEVPPPMGhYsmMpDDy3l5ZefxfMMv/jFmxl/LTX4iGTwC5TlLxTc9oalX9KGAM2pHhpKOaVUugoRFuJw5oPxvH5sCqA5lW/ycqIKEXHw62YDtoiEgLgxpm0X8s+Bn4nIL4HLwHeBn2WzrgNRV+HT3xfiFhevaDNW/c72/haJFDB37iTOnOl+HSI1mCWAKKdOTeZqQQ3ulHNA+/Hp+w+cZuWKOf7sR220n8K2a3qx8OCjOdW/hmpOKeXzgAY++WQMZ2pdnBUnmUsIF9i3bwGhf/ccUMDJk+8yb95izak8ka9nqr4LNODPjvQ7yZ+/KyITRaRWRCYCGGM2A/8F2A6cT96+n5sqq3Rkp5exY9FoEx98cIHRo/X68KErRjzeyJatyzjs1Pgr0luGAmO3K5lIWCBoz53qjObUIJXLnFIK4nheA7v3LGBXjYXzyFuIm6DEOAiC51lg+av9JhK25lQeycszVcaYHwA/6OThSJuyPwF+0s9VUhmUrWD61rf+jscfv4sJE0Zx/fotfvSjf6auLsqXvvRIVl5f5ZtGYjGPF15aQ3ztWzil1VhGWCWlhDFEO3jG/9x5CveuwwCUUJLd6qq8pjk1uGkDSuVGE57XxCuvrqZ2+UHccVfAwHKGM0KEeFzA8giJ1/KMn246T+DBnYDmVK7lZaNKqUy4ePE6X/rSj7h+vYphwwxz5sBf/iXU1PwloIE59MS4dm0kDcEmgqXViIF1Uk4IG/8kQ+fSWjgxS3TVe6UGH10XS/li1NREuFkfIjTuCgCPyEiGkeD2rQI27lzE5Gde5n673eSheWWo5pQ2qtSg9ctffjdlvZE7mtcb0cAafHryxSQoVrJBBd3N6uXk06Eyuep9Wz0dPz9UQ0+pfNM+p4zm1CDWXU6lLuQbQSglxscfTWDvuTLuW7+NCfFCtmxdzLpHDrfar5VPV/QM0ZzKo9+AUpmn640MHV0v2OmPP79xowQpbT/pmnTQqLp+owQZcaP/KpxrydBre9MFI5XKLs2poaPrnPKXCKisLMIaXoWLsI4iPv5oPHs/GcV9aw8wzQry2murSSw+ijFw/fowpMzPtHwaUZExAyyn8qj7Van+oOuNDBWtv5g0M9TUHCSRiLJr92IujLqMe+9pMDBdIh3tpsWNRe/glFeCgYAE+q3eSqmhTnNqqOgqp4yp48DBuXxg1+M+upUINi4W166VYo28wUjxv7KPGXODJcWwYfNdVM09iTvpouZUntBGlRrkhI6DKT97OVRfdP7F5PnXV9F0716csluIEe6S4YyloMu92eWV4AlhqzC/hv8ppQYZzamho/OcemXjKm4vfgd37FUwMIuOr5uaNescL2xeiVm7HSfcoDmVR3T4nxrU7qwrYlJuut7I4NTxFxDPs4g/9gZ22S0KPJtHZVSrBpUQ7zDnLGNRbBVpUCml+pXm1FDSeU7VPvgW9piruJ7FWhnBRCxqago4f62E0TPOUmT8r+wbN96LeXAnVrgB8TSn8on+FlTO9WRygZ6UDYUewPP2UVR0g5EjLxEIRGlqClFXV8KFC2f76+2oHOlowU4D1A+bh7hxxpsCllvDsVpCzWARI9FoMG77/RVKYX6OUU+uet/R9s50drGv6eRiYqVUaz2dnS/d8ppTQ0vnOTUfq6CRMs/lXquMAqJ8em4MOz8Yy8rPb2WaV8Bnl4cxbmwVsZhLMNgIQMQKa07lEW1UqZzqyaxHPZ0hyfMEyzLU1ZVRV1fWajtoWA02FRXPcuLEH1JTcxhjDEaEhuL5VM7/A5YyjMkSTiltcIhSczPMhh0L+a2ndmMGxjG7dzMedRBKzYHXLvgGSHgplS09zZ6elNecGlra55RFQ/E8Kuf/P8yjmNlWCMdrZO/bFZwtquTJRw7g1JXw/JYVrHx8Z3JmQMn7kaFDNae0UaX6TTo9dTU1B/nOd17i6tVJLdtsO0487nLu3AKi0e2tyvraX+CpFBgqKr7DqVNTOHi1iMADuw/lUNUAACAASURBVLCM8LCMpJjWp6IEDxuPQ4dmsebh/TQ1Bti6Ywnuw1tzVPfsk2RP4ktNr+S6KkrlVHdZ1ZOcai7v06xS7VVUfIfz50ez7cQUgk9sAgNrpIxRQGO98Ppbq6hfeYBJo24xjAj7T05j/OJTjLdttmxdinP/LsT1h62L5Hnrqo8GWk5po0r1i/R76gxXr06irOxyq+cHgw2cPbuwzV51hiTVvUTCAtufQn2UBNo1qODOFOpr1rzDrVslPLd/LvLwW1ihRowHYuXZmh9KqX6RXlb1JKf88h3TrFK+RMJCkjlVKjbleFy5VMbWo9NwHtmCHYxR4AXAgqlTLxIIxHl+w914a3bilNSCJxRaBfk59G8I00bVENdfq7in31Mn2Ha81RbHiRGNFnawV50hKZU/Fr/9e7cs067ndGjw/zYaG13EjXVTNoExcOlSOTvfn4D7xCbEMiSujMIefa3/q6qUSlt/5RSkm1U9yanmfWlWgeZUe/7fRSzmgNtECOEBwpz5YAL7Pi3FfewNxIKRJsg8K4KfVTYv76jAfWITlpNAPIuIFdaOvzykjaohLFOruHcUeOn21BUXr8C24zhOjHjcxXFi2HaMjz9e0O6ZHV3g2by9P+VrKDSPxe9o+9ATx5gohw7P5n3ThHPXEQBGEmxX0iKGJBKIDadPT8GaeRqxDN6l0UhZFcaANciHVCg1UPRnTvnP7z6repJTzeU1q3yaU6kSQAPvvTeFQ9cLcR/cyRgcgli8//4U7GUHEQsmUMAyKcCNJ8CBM2fGY026gDgJxAhFVkTPUOUpbVQNYZkY991Z4HWu9YGgouJZLl6cTiRyi1Conmi0kI8/XsD165PaPdO/wDP9HkvLMp0GTE/kMhT6s4d2cDBAjMZGwxtv3U31gndxx18GAwspYTqRVmX9ySkKuXR1FPNnX2y1JxlzBbHA9hzCacz819lMRVimdxfp9qdezMSkVD7oz5w6ceI7pHNWqSc51Vw+3WN3pnIKcpdVmlPpiBGPx3hr+zKuTj5LYM1RMDDahFr9aVvAMgLEbwfY9c5sPrfm3VZ7ccXtUYNKcyq7tFE1pPV93HdngdfZvjrqqbt9ewTvvPO5tF6vJwfqjnrmmnvyAoEHW23P9Vmnjs/2kZEe2nzQP6FrgAauXy9i89vzkIe3YRdEcT2L1VYZw0hdXT6BS5QPT0/kwGclPLTmCLS91kogSJCgFeQLoSe7D6JOpnntMBRyLO/CU6m09V9O1dQcTPusUk9yCtLPqoGUU9D+WA4R4HZKCc2p1gwQpbo6xMYdy0ms2YlTchvLCKtkOGOxiEaFukYbKb7NOoqwm1yee+V+ZjzzGolEiBtVRcikM5g2E1Ok1WDSnMoqbVQNaZkY9915sBUXr8xKT11PDoTZ7slLZzhG92f7BvYMUpkavtNaAmMaOHFiGkeqA7jr/WuihhuX+6wRuCljzf3hfnG271rM5bEXePDBM5TWR6CwqdUebbEIEfLvDKAgUmpw68+cMmmfVcrEGaV0syofcwo6PpbfaVBpTrXnAfWcOTOOPZ+MxH18I5bjEfEc7rfKiNDEjasRthyYg3x+A1aokbAp4XZdiBUrTjC2sYTnNi8lvno39vBqMOCmdgZqTuUdbVQNYZkZ99154PWlpy5dqQfCf/5n+Pu/Nzz11AG+97386CVLJxy7P9vX1sA5FQ79Mb1wE7FYgq3bVnB9xocEFpwDA3MpYrYUpQyNMNg0Ul8TYOP2FYTW7OYLJR7nTs2ksqCWRVNutNqrXvSrVP7p75yC9M4q9fUMUf98ac+MdBtxPcuqoZ5TMTyvid17FnGu9Cru2h2IwBRTyBKrCMc0cuLYDN6ttXCf2Ox3CnoutiUUFdVSXV3ES/vm4jy2CcvVySkGCv3tDGEVFc9SXLySOwcRobh4ZY8O8HeCzaTc+v+C3GbNB7xTp2DDBpg6tfX2gaGn4eP/vpp7TtveejMWv39lKnT94X43bwZ4bsNdVK56G2fqOSxPWC0jmENxq7HmFnEcEmzdehex5Yd4sMSjoXI4h05NZuLkK+32rhf+KpV/BkNOQdsv7c23wZxVQzmnotTVCc+/dg+fzj2GO/99LOAuhrNMIkgszhtbVnK85CqBew8gYphJhPutIjDQ1BRk265FOGu3IW4Cx7gUWRFtUA0AeqZqiOtrD1lPJ4/IPENtLTz7LHzzm/Dzn9/ZnimZvJC4Y531ojbruIc212Pr05eJ4Tv+rEkffDCB/RdLcdZvwrI9ijyH+60RBLE72Lv/mtFoACmMEkRoiFs88/gern86jtJJV1uVtzvYh1Iq9wZ+TkE2zujkNqs0p/zn1/Ppp6PYcXIi9rrN2IE4Ic/mfquUYSSorgqyafcCzEPbcSL12EZYIcMYjyFW71KTsPDiDjgJxPbrE5bOpu5X+UYbVarPcjt0Qfjxjw2rV8OSJcLPf25atvdUZ6Hb36HQ0fCW73//Vxw9ugaAkSMvEQhEaWoKUVdXwoULZ/u1PpDZC3b7PnwnhjGN7Nq1mPMjLuM+tBMMTDNhFlolaZ5hMljAiBG3eGvbCpx5p+h43q4eGIAzFQ2omaCUyqBcD7HL1NpVXR2bs51V3/nOS9y8WU5VVTnhcM0Qz6k4EOXQoTmc8hpx1r2JCIwzIVZYwwgS5eyZ8ew5O9JfbyrZKXi3NZxSYly+VMbWIzN46vG9UD0sc0uaaU5llTaqVM61PjA2S+8AuXXrZC5d+oQ/+iNaPb+zA2FnPXnl5ef6bbx7dxcXd9SLevToGurqygBa/n9nX/0bVpke+9/3XuImbt4s5uPLZYTu9780rJJSxlDQ6TOEOOLF2bV3IYkFx7m3JI6VCHDtWhm1JFg3pobjx2dytegm7tRzQJtrqtIIotSDe6sQ8ISnA0+2lM+rENALm5Xqlb7kFPTsS3s+5hS0P5ZfuTKZuroSrl+fxPXrbfc19HIqGnU5cXI6wX//C0RgMcOYKmFsGhFg374KnKdeRWyPcaaApVYhhV6Md47M4pTXxPLHd+BEi9m0ZwHu2q3dv6TmVN7RRpXKqfYHxmbdHyA/+OACf/d3Vfzt3y7AdU/QHFaBQHmnz2k9417qtLAdneHK3gxGbet7+/aInI05z/wFu33vJU4kLMT2AHCQLhpU/uQUjfUur2xdhXXP2zxdFufyR1OJTbpMKNTI46vfZduWu3jy8+VgBJjZehe9CZgBHAJKqa71Jaea9eRLe77mFLQ+lp89u1BzKoXnWSAGSfbPTebOsD0BjLEQy8+xZRLEjgqvb7ubhkVHeWJsLQ0XJ/DC8cnYn9uCHYiBZxGxCrs+c9P0Sg8qqDnV37RRpXKquwNgV48fOHCKGzeq+Y3fOEnzhb+JhMfx49d44YVHuHXrdYLBQLvndTwtbGf6HhgDbzX5fJrNyb+ovKnJATfWbVmHBi5fHMG245OZsG4rq6wC9u1cxsVRnzHVjWOFo7zw8hoSn9sC5mkNGKVUt/qSU6l68qVdc6o7+ZZTHrGY22FOCYZ43MIzhoCdYD1FWA1BXnj1XkJPbOKpQodPTk9j/6VhuMlhg45xKLSSi9BrY2jA0EaVyrHuDoD+4x318H3+83/IkSOtzzL87u/+BdOnj+Nb3/otAgG3/e7o6bSw+XHQSg231AUh+2cxyMyM/e87f42Ps2fHsPvMaAKPbwRgeNtFe/GH+zmmkUOH5vCBVc+KdXsYXz+Ml7cux129h6eHJzBxh+vXhxMNNhIsud1uH0op1bHe51Rvz35oTnUnX3LKn5zi8uUy3nxnBsFfexEMFImNYLCJUltdwMYdiyn9wms8HAwR9CzOXS4jVnybqeEYQQKcPj0Ze9lBRPy1qAp1cooBSRtVqtcyEyDdzXwnnY6dvnDhz9q9XjgcorS0mPnzp3Sxz57PtNcs3UUS+1Nqj2J/9CRmZl2YvvLX+Nj79gLOFlfiPLwdEZhkClkiw1LKGSxixKOw8a1VNC09zPrR9dz+dCIvvjeRSY+/yd1SyO7ty7nnvqNZrL9SKh/kOqd6f72T5lRX8iOn4hgT5fA7szgVT+A8thmxoNwLcbcMI0ADZ8+MY8/ZUcx84k2WemG2bVnGIw93fWbT0a/mA1ZeTnovIqUi8pKI1InIeRH5rU7KfVlEEiJSm3Jbk+XqDkl3AqT5gHYnQHqiuwOgf+DM9PoeXR3gu14LpXl9jba3TIdG27U9sikT68L0nr/GR309vPj6PZyb+R7ugvewgBUMZ5kMx0pZ3NehgaqrYZ7fsoy6B99ifXkDDZ+N5q2j05nw6HbudwrY9uZKLo3/BHESWah/nkte2Nz2ls8zQeUrzan8pzmlOdV/mmhqivP6prt5f9QF3LsOIwILKOY+q4Sg18iunYvYG/WY/vAeVtkFvLHpHmILjiNiuHKlDBl1lZHYNDQEqGt0kJKaLNU9zw3gnMrX5vD/AJqAcmARsEFEjhlj3uug7D5jzL1ZrZ3K2EWi7S/ebXanR3Hv3rWdPLv9P7CtW3/S7Wt23sOV+QNyb9cNadubGAg8mNULgnMz/bA/jOLixTK2HZuK/bk3sIMxgp7NGquMSMqwPyGBbaL+ivR1/or0AQsKKOBMZQky/BbDxV93qqqqBOuuqv6t+gCZtjavZnga+DSn8ly+5lQ6NKe6l7ucauDGjQib986Hh7fhFERxPIvVVikjMETrhI1b76Hxnr24I25SZkIAjBx5kyVlTWx64y5qZp9i3eQqvCtjee7gLKwnX0eCjWAER/rpq7nmVL/Lu0aViISBLwLzjTG1wB4ReRX4EvDtnFZuAMjeAoeZu0i0+/pldux0d7MwZeozzNUQjHTrn9shIiblBuBhTCMHD83htERxHtuCCIwxIVZapdgpv2uLGCbmsfmtlVTOfp/Aok/BwHQiPatCBgOmNyEwkNfiGOo0p/pGcyq91xvMOQXpvYf8yil/JMXJk1M5XFmAu34TYhmGGZfVVhmFNHLpwgi2n5iCve4N7IDfKTje8htVs2ad5/mNd1PyyA6eLrA5dWQeJ6IG54lNiAW25xBunpyi1ZvNTFZpTvW/vGtU4c9vnDDGfJiy7RhwfyflF4vIDaAK+AXwZ8aYeD/XMS9lfkx3VzIbIF0dXPtj7HRnn0cmP8PmIRgdbe8vPal/LurnSwANrbY0Nrps3rqKmkVHcMddAQOLKGGapDaUDA5RqqvCbNq1ALM2uSK9J6ywhjMGaGhwOX12DJH79jKNAB9/PJamsuvMKKrHihdx/ORU7IrjAPxT9J8pprif32sXejGjkwZc3tCc6iXNqfQN1pyC9N9D7nLKnygpVTxu89b25VydcobA/UfAwGwizJNCXBPl4MG5fODU4azzOwXLTYhlVpgICU6fnsSBy0XM/fxWKuIRtm5eTtW8E7gTL2EMBAkStIIdLmaf02O75lSP5GOjKgJUt9lWDRR1UHYXMB84D8wD/g1/Wes/a1tQRL4KfBVg4sRRGaxu/uiPdRs6k8kA6e7g2vdF+dKXzc+wJ9IdnpGL+vfsdxMjHo+zbfsyPrtZTMvfT0EDzkNvYRc04noWq60yhpE6HX4ClygfJoPJWX9nRfq7rFKG08TVSyPYenQaE9a9yb1OAft3L+H8sGusfmQfpdWjeG7nYrwHdvCVUU8P3AO+Tq2bLzSneklzqu8Gek5B9t9DT3OqZaKkz0Zgmv9+3BjOgztwSmqxPOFeazjl2MSjhle2rqJ2yTu4Y6+CgfkUM0McnJjhrd1LuDruPA8+8BGFleW8sGe+3ykYbgBPiFiF7SanGNANkyGcU/nYqKqFdt3HxUC7OZCNMalLdp8QkR8C36SDsDLG/BT4KcDSpbPyawBpxmRv3YZMBkg6B9ee7LdvwwW6/gx7O/a8r9If5pDdtTvS7zH1h00cPfon1NYeprDQY1qh0FA8nxsVf9BSqtRzudcagZsyh45FDEnE2bZzCZ+N+xT3gWNgYIoJM88qJGyaOPLObE55TSx5bAczYkW8+urdePfu5ckRMarOTeLlUxNwH9+I5XhD+oCvMkZzqtc0p2Co5xRk8++gZznVyLvv/jG3b7+DWB5Tx7XPqYjncL9VRoQmrl8p4s1Ds5CH38IO+p2Cd1nDGINHfU2QV7YvJXD/Lp4aBpc+nMZb58twn9iEZRvEsyiyIh2endKcGpjysVH1IeCIyAxjzEfJbQuBji7+bcs/ag5Z2V23oW1g1dQc7OUQjsweXPs2XKDrz7AvY7izs4ZHc/3bvof++RtIr7fRH0axb99fkEgcbFltXjAU1pyg/MRPqKn4BlMIM9sqSgkYg00j9TUBNm5fQXz1LpzhNYgRlkoJ43GwGg0b3rqb6gXv8rvT14I3P+V1n2j56R8uv4Y4Ho7Jx0OeGoA0p3pNcwqGek6l6v+sSi+n/ImSDh78zzQ1Heo0p8ZRwEIrgmMaOf7uDI41CO7j/jVRpV6AFVYJw4nxydkx7P5oDM5jm5jpuhSaIAcOzOerX3HAfKl9JQfCGSjVrbz7hmGMqRORF4EfishX8GdVehJY1basiKwDjhhjrorIbOB7wHNZrXAeyfa6DZkb150vi/hl7zPszRoe6fW4Ruigszy5vT9090UjRiLRxK7di3HcfYgArfrlDKGaE9zLmDbP93Bp4FxKMFluggLPZpVVSilxbl0Ps/ntecjD27AKopD8kmJMcu9iEJKfr+0BEJRg5t56FnQ0BKS5p7KjXkyVHZpTvac51Xf5nFPQfVZ1NaV9//wddJdT/npTBw/NoSl2oCWn7vz3Tk5ZNGFiCX+ipDmnCEy6AAZmEWGOFSCYiLNnX3J9xUe2IQKOCSAiGGOBGXxnoDSn7sjLdaqArwEFwDXgX4HfN8a8JyITk2t8TEyWewg4LiJ1wEbgReBHOalxHsj2ug2ZWpfjzkG09Uw72V3Ez5fJzzCTa3ikv95KbSd7aL+9bf2abz0bItLZ+xKgjtpa4YXX7+FixVEEr5NnmDbPjON4UfbuWcDuWssPJjfBOFPAA1YpI0yM949PZcPpcVjrN2EVRinqoH+o1es4sR68pyxJZy2O5BCQ5pvKK5pTvaA51Xf5mlOQXlZ19dm3fQ/9n1P1NDYmeGXDKj4cf7ZVTrVpJuBSx+2qEM9tXIlZvYeHJ1Wx1kR4WoaxCAdT5/LShjvrKwIMI8hICRKP23jJfQ8omlM9kndnqgCMMVXAUx1s/5SULndjzDeAb2Sxankvu+s2ZGY4RDYv8E23PpmQyTU80r+o13RYrqPfSSaGcnTWYwr3ceTITE5eLcZ+bDO2G8cgWF329PrD/RrrHV7deg8Nd+3DHVXZMhPgJHFxYx5vbl/BtWkfElhwDgxMJMx08aesbT5LJWJa3bci9WDAkvzpR+rrUI9WPZtDPMhyQXOq9zSn+i4fcwp6MtSufZl06tcbXeXUu+9O5cSnI/1rokKNmE/a5lTzGUqLQwfmcrrBYu76N6mIF/HxexMAiEz5jMtVxa3WV8SDSVaEuThYt8L8audCnKdeg698oc/vJ5s0p3omLxtVqnP5dFDPp+EQbS/6Tf25vy7O7e530dGFyM33e16ndL8YZPt6hT/lxIlvU1NzFGM8jFjUOcuBRryGuUwtNpiDgileRFHxMupqDrWrX7h4OWBwaOBym4V/A57FXdZwRuFRfyvEKzsX4j2wE6fYn31pnlXCBCyCDXbybZo7TTQjIMYfbuFZRKwwFlZWF0DsywxOT4c+D3Q+LOSlplf6XD+l+oPmVOdSc6FtPuQiqzKbU5BeVuUip/6Qmpp3MMa0zqm6CqaUGcwRIVG8oFVOGYOfIUCts5yqSR+ydnQ9wRujeX5vBcOXvsvaMXUcOzKb901jy/qKtifMsUqYBZz/cAJvf1qK+8RmLLsHZ6o0pwYkbVQNINld36N7mRrXnYn3lTocoK8Bkc6sTOnUue0Qhb4NrUgvhPp7rH37cF5MIvELPsLCXXUQsQwjTvyYwpoTLc+xMFBzFIpXEC5ekdKwEsLFy5lb8cc4pp7Dh1sH0ygTYqlVTDFNnPlgAvsuliZnTfIo8GwWWsMZS5xbV4bx6sFZrT6NlgZVUuoMS1m9GLgvMzgN4DH2aujSnOpacy50lAc9GdaW7uyB3dU7szkF6WRVLnIqGPyffNRQivvAbsT2WnLqzgBKg1VzDLt4CYY1GHYh4mHwZ/+LVfyfPE0TZ9+bzaHrIRas38rcWBGbN99NzcJ3ccdfxhgoIsA8K8yEBOzZvYgL5Z/hPrQTANe4ab8HzamBSRtVA0hv13Xor17DTA2HyPR6FW0DoqlpW6dlO6q/5+3rdlamtnX+1rde4/bt4bhujKamIE1NISwrjud1/E+sp2fS0g2h/hyi0lE4V1cfodb93wnc8/tgYCYRGmtOJh9v/fusqznEsnu2tNpmESPR6PHa1jvBhIG5FDNdgoQSMXbtXsyF8ku4D+5EgDGmgNlWISNNnONHZ7asSI/1pdZfCJonrLBMx1PWKqUyTnMqPW2P+11lVW9zqm29v/Wt16iuHkEoVI+IoaamNKM5BellVU5yyvo67kO/hwCTTSGmJacs/4wUAB63br3LuZmvYpd/2Z+AQoqYjs2IuMW27UupnPoRn1tzHfvaGJ7bNwd5eDtWQRTxhPFWhDnYBG+HeHHbEpru24tTegsMFEohLm5Wz0Cp7NNG1YDS87Hh/d1rmJmeR9Pm/22394/OPpuiohvU1ZV18+zWdauuHsHkye8Dhps3y3GcGJWVY6muLqO7+WDSGTPeXQi17rXc1/K8rqbBTT/UPFp/eUjOh2QExCMcP0i19zXutcoYQZDDXfydComWezaNVF4r4Y2UYHKSa3yUA423HV7YtpKm+/bglN5CjDBHSpiITbhJ2PzWXVTNO4k78SIY2BJ9gTUEOHFiGu/cDBFYvRcHhzDhbj/fAUcDWOUtzalM6ltO+eWbNeeU51mIeBQW3s5oTkHXWZXTnDL7MXyNBVLMKBwOpg7vS2njiHiUld/E9RwWWCUMw6PhViHP7VhM5KGdPB0RPjw2m6M1Lu76zYhlCHkO06wiZmK4dr6cjacm4Dy2GdtNgGdR1Dz0nCyfgcq1IZhT2qgaUHo+Drl975pJbj/A3r0P53ise9dTq7Z9X90dXHs6bKGznseRIy+lEVatfxfBYAPGgOf5p/fjcZf6+iIAHKeReDzQ4XuC1muBNOsoZLr6PfV0zZP0vsQYIAotDaE7+08dXicY1lnlBEle19TF32mAaPL5cPz4NC7f/jqTR+xCjhoMQkHxMsZWfIdL50ez49R4nMc2YbsJAp7NYmsYY0jQUBnm+ZQV6cUTJlnFTEeorw/x/tkx2PfuTQZmpx9Zr2R7lfvOhmQMqWBWA8zgy6me6K7zC3qWVX3Lqebn+Z9nMNiA51nJOjj9klPQeVb1X041AbGU+8mfkjnlz+RnWCdFgKGyMuQ3LK3W1zglpzniMYqSbcwEZ86M48yl/5dJI3fAccNhz6IusILAff8BDIykgElWiCme4eD+eZwpqMF5dGtyKnWXQqsg6yMlNKdyRxtVA0jvxiGbTn727+dyrDt0PbVq6vvqn57MjntRAoFot89s+7uwrDgADQ13zor4vYEGx4kTjweSM9IZf62KFL1fALL3uh/K4gENnD9fzu4Dc4mJx/TRfu+m37NnUp4tKQ0qf+KJupYvFXfE4sP5x+fWtNwfM/FRIrK/ZT+CobHmEAf2/jfOhH/cEkwjTJB5VhHlxPnw1GQOXgvjrt+EZRmCns00q5gZGG5eHMnzxydjr3sDOxADz6JACjL2mfkfS9djz3O5Xke2g1SpjgzGnEpXOtcv9Vzvcwpa/z4syz+bI2JaGlMDO6cMUM/Vq8N4a/ciGo1plVN3ZoEFY6yW/JHyq4wZPp9wzXH8ERR3tM2psePWUeTsa9mPWB6R+H6sE/VEKn7AVGxKGlxe27qcuuUHcUdfBwMFFBCQADmhOZUz2qgaQHo3DrmzXsM7j/V2THhmdP4POPV99W08u8HvxWr7Wh1/Nk1NoZafR448z8SJpwmF6rl0aRonTvwhFRU/oKLiP3HixA+oqTkMGDzPJRoNE4vdWVzWceI0NjrE4/4/s5ZFaWl9CqWzYAqFHsjoCvapQy9mz367pa5lZZ/x53++PqVujXhejH3NvW7PvMi4kz+Gmo569QDC2DS13Jtf8X0O7P0NmtfGaj5j5DhVjJt9Hzcq/oAiLIr2Hmi3J39mpp24i04kr9EqZoq4FMc9tu1YypVJnxBY8y4YKDMFTLQKmInHu+/M4mSjh7PuzWQPoUOhVZj9a6nahFnq2PkeTy3b07H3fbnYWKkMGZw5lZ6+5FTn1yz1JqfuNEBb/z78/dXVFbcc/wdyThkT4913Z3CsHqZ+4XXs9/6iXU41509CQgSfeRkbmEwA1/whDW9/Bai7UxZwnSpmzl5DQcW3AGjYuz+5HytldllDuOYEC7GouTyc5w7Pwn74TaxgE3hC2CrEyeev15pT/SaPf+uqIz3vqYsAt+l63Hcux702B0brEGo//KD7cfqd96TVU1sboqEh2OqxYHAljY372+27rq4EzxOKim5QWnqF6uoRVFU5jB59jpqaQxw+/CdMmvSfGT36Lxg92n/OJ5/MZdGi6zhOjHjcxXFiRCK3iMdHUFJSSVXVaL/3LPmePc+mO5nuBUwdelFUdBPXbSIed6msHJtSyuLatQh7Ds6hbvkh3PLrjN37NRzqW+2rdc3quXm97XVLfvnmIPIZCmpOMocg8+KFrPtPv+Kzz6a1q+fYsWf4vTejLLRKGAckqoM8v2PJ/8/em8fJcZXn/t9TXdV794xmkUYaabRYlsaSRgvyghHYZjOLMcQmIQmXxYTcQEJyEyAEm8WAsAl2Agm//OA6CwmXhIRLwAYDtrElywaPjbVYkkeyR7IlpJFG0qwa9UzP9Frn/lFd1V3dXT09i0Yz43o+GHWfqjrnVPX0efp9z/O+L9kbW3XLswAAIABJREFUnkStGUZIwTIRIYyXJjKQVTh4cC2+938PIcCHD7/wl/Q7Xahm8Xc8ZwLeuPngtXPxysT84ymojqsmz1NOhonTzl9lnnq2xLACOHasjUhk0NqFmss81dcXYve+tcTWHebtW87zUvtHKCxyb8m/c9PzMEYjKq+VQRJDNWQyHo4yljvXbjARe55lfYsB+D1HnjrGPfe0cjiTQb3pYYQCHl0ldCmceWXg8tSlgWtUzWMY8UrDDkeFw+uZhXNRvjDt7W+mvIFViPHnvmfvWg4PhBDBsaIj32URf0TITJ0qFeJcx13/mQFeZhWtOYLJyyCkhLGx3TQvva5kyu3t7yIYjLF06UukUn7GxsJoWppk0m/JD4SgINPQpYKgq6uVNWv2WS0yN6kReR0Pv7QI9c078HhTLOz4OlqRQVUI4zKdhzqX2NovE3qO0PI3a0r8lg8u5Ie/3Ex3dxdNTSfJZPJpZlU1TXf35VyrnKGJNF3Hmnnq+ELUmx5GUbNouoeVSg1q7jMR1hwE5PTxXi6R5GIcCCePngsXr2DMBZ4y4GT4Sdrb38RUecoJTjt/p04dB4478mQstrts2vW5w1NU5KmHOhdTf91T3BrwsLf9/0fKEfvFwv5SILkhU8POx7dyTksg1IzBU1BgUIncGHlO6+6uceCp1bzY1IW29AxIw5nnU3yzwqCaClyemhpco2oewyn4t/j1dNWGmF4Ukqx93p/+9M8YGlqIx5Ph9OnVDA83AM5BsJ1NJ/FeebbsKIPcxiC32do0ngZAtJseO3ufQuggS7XHEg8jI3Xc/vwRAP6svpmtW58km9U4cmQr6bQPISTDwwusYOHpQnHdk0ikn8bGbpqbX6a9/T1Eo1uB3Vbgbm/fcgBaWjrRtCRSGLU4BttuYxPPsRQvmvRxqKDelO1eC7yAEoH22qftx9uLq9JbM+Wnzy9Hvfln9H34bTQ3vwwYiT38/jh+f5xQqJau9ps4KUEKhaam9Qyon6RW+lhcUG8KJCqQSnlBS035N9dUtd63eN9lzGqWEtJ817K7mJuYCzxV2fAzMTWeqoTKO3/Ou2PlxzJ46vnn40Cc+lnFU1uAvdbxviKeEkLgi26mte1PEXQQSvl59OGrqa15N0oVy+5///zVRN74JG8JSzQER9rz0korqYUAENz02ucB+ETfdY48teLkjYiTBgemo5sZa7tzOh5TRbg8NbvhGlXzFAYJ5GVXBoq12Ze60r2zBr1SWyxWT01NP11drcTj9baFukSKICRK81kUXbBQ8Rf5PSUBdOLDARKJ0t0No7pRGe3vOL/ea/saARgeqefAgetpbOymqekEHk+WbNZDMBjj3nvv4VWvup3a2rcUaNjLwe6BrFTw8cyZJzh1qonR0Y8Cv7Qdj8X2EokMMBKvKzuKAoRQuZYoY711nOpeWDB+0YyKnaLRzbQQtN5qgC+6mXhsv+16KWHEcw3aDU+BhOHhBo4e3UpLSyeh0AW83gTptA+PJ2ONIdAJxToIdHyDaNuXC+YrWUWWkXN1PLp7Dd7fud8gRCl4d+C3Jrcoz3Ot97y/PxdzDi5PGZiokWWHUzxadd/reNyZp+67b3uZVOjjoxJP9ffv5PjxxcTjf0IpTz1HJNLPyEiDQ8+CZCzCqf3rADjc1Uj9jbtQnhv/+UkJl938GJuzYV46tIJ0WgVeB/zS2J0Shcz+OmuMSjxlni+QeGP7oWN71YbVpI2H+b6Oz/H7c42qeQK7PMAJ+QV/27bHZmgu4xHi+PN94AH42c/g3DnweAIMD38c+CywouQKRZHs3v0Cz13woW17BqFIAlJli7LAkowBXOa/sfyXVEi+fWwnAB7tSv7mM5/i3LkVZLMqHk/G8jpWwkDSkAl8+9gORO0QC39zF8GiHR8JPPmMlxMnwixf/trSRUSKnPwiDgTo6LiTWGw3a9Y8TSRynqGhBmpr+zl58gpGRyOMjkZZsuQGAFat+gZNTSe46+5bbSM2Np62jKqFjSdZs2Yf2ayWCwSWENvP8+3f4CXuw7P8FAArTpYxLItkFRvavmq995AhqKd5JvbfjPAhQjyFwEiXPhrazOCWjyCkYL2oAQxPZF/fcrZu/QWpVIBgcDinb7eP4YkdsN770VkldQ5ZhX8fsenZyy3KUhegC8tLZ2EWeb+m7KFzi0q6mAMYn6vmJk8VYtu2x+jsvKZkZ6YQlWozmahknJjXRqNX8/GPf3LCPGXC7Kc0cyHEYkZ7InE3Xu8bxs0AaD7TSjzV0PBGwM5T1h6lNFLFj+RSxRfzlEQHfklv+oP0LrmL9ZsOsi4dYU+Z9OjFEEJw+dAifvSrjWQ2HkJoaeBzLDz7WcIpM+GUQly9hr5lnwNO565spbdvOb19y7mymKeKoMUOUBxk4IgKxkMJT8Gs4SqXpyrDNarmAcothpVwMWUUE099Xinrk4HGRvijP4KlS2Hz5m+xYcMR4BY07SkikaW5VLAqsVg9mYyPA+EBvBtPgITlhFgt/DSQtpU2lLowtvlF6Y9vz9IzAAws/Rj7999Aa+te/P5REokgXV2t1oLvBPN6EwEHCV3It4NHT37D0K87GHg7H38VCxtvzWUZhHTaRyAwQn39WUZHI4yNRfD7R/F4svj9cfr6ltPcfIwzZ1aV+Cmbm1/m+PFNxtihC/T0LCebVamvP4OpJZfil9y07RB1uaXhhfObicX2V7zfmgIaGRvz8eMd2xi9+lk8iz7MIB+2nesrqDdVCL9/lGQygKJkbDt3BTmJAKgny8KUh0d3Xs3g+g60lm5kGT17Ja+WU9aj6UKhJn1C6Wmn6KGbDYTrwkUlTISr5hpPOUFVk0SjAzae0nVvVTs/1dR1amu7i/37N5Xw1Pg1rOwxSE6fi9E+4mggGvMboaNjO7HYc8DkeUqIyjwlcp9DOL2XG5eOggygaxlqazeNy1PR6BZqaof53Xc+ZXfaLf0M5840sOO5y/G8eSfCl8bDmbJ9OPGU1dcE/0aq5anxzp0MXJ66OHCNqnmAypp0O6LRayYso5iIR2+iKWWdE1Xk8drX5l+vWbMU+ABwH37/ThTl/WSzKoqiEwrF0HWBuuoEQhe0KTUsQ8E75mFX+1ZGRooywonyYy4nar0eHm5g7763OM7N6gqsRdb39KtLjjldo716D//80hl8e67G5zEMDSGgtfUEy9b+hl8MKXg7nsvpvA2zUNOS6LqC15sEjMBiKQUtLZ309q4gkQjmJHT2z+CLX/xdBgb6AWho2JJrV3J9GHpyBUnXvg20n1qUO76TBQsuw+M5X/YepFT4yY+vs95f8KRQb3wUjy+NpitcpoQLkkoI6hUPC2WGzsMrgfyPg+7uy1DVNIlEkKamE2Wem2AlGTIDEX74VJtV+BddEL4I6WuLCaKs53AS/cwXb5wLF5OBnR/mF0+VQ2PjSRKJEIqi23jK74/T07NiQn2VRxYYM3hq71vLnuEkL/9xwboN0OCkuMud+/3vX6h4vL7+wMzwFBKQ/OTH1xdcV56nREHXx47t4Nix0rnrEoaXnEZ9e07xIFV8hfWlCoyPcjxViPFCA6YbLk/NPrhG1bxApS9AoaElJklUE/HojSfpKO67mMQqk202Gwe+D4zg8VyFlAojI7Vksx7r2j+pfRe67iGT0chkcmnUheTfhr9f0NPvO45Rj2bb1Sq3TErsC5E1YyEJh36X4MjzluytEqQuUFu6ySz5CRkzfa2EZ3+zgq6dV3HT9fvZo+hmKC0AHk8GXfdYBYfBKODo94+CkHR1tVJXd64gNW5uatGNNF77qPHm2Tw5FUKXCueaT+LbmJfbKbuHHOefiWzE9+pfWO8XqhmEgIj0s1IJoxTcvw+dUFry2ONX07fqJf5t+L+sh9tw5J5cIcbyzzsc3UK3Wfj3HQ+jeCRCV4jYEldcPJieuWq9caYX8IHUT8oed5JQSAcvoAsX8wPjcdX84CkTLS2dHDlyZQlPjY2FUJQM0ejVNp6qRhKYR4pMJsvOXVflplRmPpaMvHj6ksDK11fNU/G3PzzubBr22BM7TYSnCqdYyClytyk/L04KJfDd9AtbWzFP5SWFEPdfWeEeJJo3AxL8+PEJe+mVwp2VQMd2I3bKAenoZsdjMwGXpy49XKNqXqDSAj+17EkTL2ZYXdCsswzETFtrv/b4ccnHPgbp9K1AGJ/v38lmX0U2a2TkUZQsmpYgnfYTCMQRQied9uH3x+ntW24sAt60rdeFBQUTTcnE6GiYgfZ3YpJ7JNLjKKNIpR6ks7OFZ89GUa97GuHRaej4mi1+ajw5QMsztzEa3cBA2yeBvBHUlP4ofv8hdv/ayEZU+PRM3byuG1/fkZFaMhmN/ftvQOqCnp4V9PUt5a/+6ufce+9NGF7bLbQVBNB2RLfkpBr2+dXWbOZ1TQkgYLW1V7iH12/6YvkDAiBjazp/PswPn9yE/sYnUCNx27GBtk/gb/8YKvZ2EIQjW+np+THnlh/He8MBpASv9OJX/NNrUFVIJTsR6YV1riIreg6rKr44CThq3p36dsnRxYxhfK6aCzyV37ly6tswkIaHF5BK+ZBSsfFUNmu8DoeHLJ7q61teVl7X6MBT7e03IaUkGDR4ykmW/p3Ud0vaJsxTe97HWHQD/W2fLOknEDuUM8xmjqdqolt4nRawtZXjKTPh0cCVf4ogXXI8f6IgJMZXPIy13Ymn/X0oxAtcAMxo9r/5wlNQ2WgrwRzhKdeomgcYX5owlexJ1Xv0Ks2lmCidsymVS1srWbYM/uVfrubF1P9i/4v/xSNf+yitrV/C51vNCy9cQzR6HiF0enpajCsKpQa5tKxXUG+NFon0WwGwyWQATUuxbt2z9PY2W7UwQLJ06csMDo7Q27uiZFbf+dH1KOsPo73+eZAQlj5CsUO5o0qBh9A5gFYgCV3oQO78Hudi/wlAU93vEFQ7rMeT70dSX3+GM2dWEQjEGRsLk05rZDIampYknc7LG3VdZc+et/HSuZwc4hzsO1o48k6aan6PkO+xfI2u5Jt56dz3i86DyxYqJfW6wLjmOz+6nqpRN4B288MoHh2v7mGFUoMn94mMdnwJSgwqCAav5IXfPIxZ+NeoVh9AQys514IiHY2KSjA9kpOVUJhjF3o2p9TXZFFB8+7kkXThYiZQmavmDk9Vigsz+0gkdtHe/mY+8pFnqa3ts/FUX1+z0VMBT5npwwsxLk8Jg0NMnjL7KJTUpe6/uaTfQOMf5G7JvqaXRW6MYKyDuif+D+fO/wCApuh7CfoKDDNrSEl9ffeEeOro2fPG9dPJU8LI6CcRqFIlKII4QlCVgy7QsR1PAU+ZV0zKoHql8xTMS65yjap5AKfigNOTgrY6j17pXMYLRq5GBpJHVn8Tydd+ksuX9HC3/0GEfBOHDu3B5/sgiUSQZDKEomQRBRIIKXNSgxxMKZqKpLV1jxUAayIcHmLJkuM2IfaCBT0kk4FSCYUi8b7b+NILKWgVQRah8RtkzjsmSx5RaYr2vDQz5N+B940/ByDU/qT9uDDOkcBX73mH0YMOigJ//df/ylNP3Uoq5ctLHQtgzrEcBvl9BotkkF5Kzx/rWJ/zatpJd6xmPd7XTXDhk7BA+mlSQnYSi5kFHu0/XkZG9qHnCv8KXSGshFCoXDvlgcSD3OJ9V3Uetkl6vxyJ4GIE4U7BQ+eouZ8lmaRcvLJw8bjqUvBUeZj34ve/nlCoB0WRnD69xsZT2ayKqho7+cU8BUnr1UR5qqenNDbGe+tPS+boVIPRaKnAU9oTeG8xeWpH0XHjHIOnbi7I4QhfHYenfL998XgqFd1MSIQc+58INCsLrf2eJ5T1L4dyPAX5NXs64pzmKk9BjqvmIE+5RtU8wcWq4VGtR288lOrbq5MsgrFTc/baD6P4e1B0BXRjyde0Ydavf5qjR7cSDMYYHY3YyEcInUTC7p2KkKVFh3vvNb13uQVMCrBikPJf8LvuvgWA7m3/Bz8CsiLvBcyo+HSVDbFGfLpKIqGhO6R2lQgGrvl3Gp59X8kYxjtZ9rX9HBiNbiQYex4U4yndcccf8Cd/0srgYJPN01kc81UNVERZb92Ftk8hOv4Wf6zD0t8nom1caPvLSvtFZee/SIR4k/+3SubW2vo00egA99xjfC5WIUZFR6hZNKkRUALVy/0qpG2d8iJ9kVPCFhuD00kqE9Xcu3Ax3bgYXHVpeKoydF0Qj9fTmEsLXsxTPl/CGKGIp3p7w9bre++9Pjd6saLDaDNb7777FqSE40t3oq44BRICWTWnwrb/zJNSlDGczJ4Fp679Di3PfLDg/vOolqdS0S222KPb7/gDPpbjqd6iHTmpl+ediWKg7S+h42sECngqFd1MYpKSvHLSNIOn+vnqPe+0tU80658Fl6cq9j8Xeco1qlxUxGQ8i9Xo26vNpiSBf/pnWMnztMZ/TLr3RerqHmNw8An8/m+zb99b0HVBIhHC74+j5AwaIXRELhjWxBIyBOJ+7t9xJYvrTOPH3A3Kz+P2T/+UgYElgBFsm8loHD++CSHgHx/qQvjznkSAx80XgThNtRtyCRfs9xXnei48exX1KIgyMguJoIYa67UT4eXTswvr+WSzqpVRybh3LI37OiqkdCqDeMcXc7tGObV4dCuhti9C21ds50WAxgn1XHAfZbb8jZomRsFh06CC3D0T4HcDvz2h2hgXzbs1Bz1nLlzMd1xqnspDWPNobTVqNXV1tVbFU6az7uGXmqzeVpXdNTLeO/EUQvLPT75AOlta0B4ANUUwspHQ8MGS+4pzHXrSW5GDquGpcjs6Fk+VkTlGCzLuVoNAx3a02AHLeDLld+m2L1WKnJoYHHlqUcmpZqKPidZwcnlq/sE1qlyMi4l7FsffgSpHgoXHzQBTgMFB2PXev2VwIE0oBMnkEa644k4aGiIcPXqSnp4VJJNB0mkvwWCMsbGQlf3vwgXjp79QJGNdi3j4cAuetz3C2JHyUgEBDA0tpL7+LKqaxuNJc/ToVqOWlATvG5+oeOcD8hPw638kqP/aWvDHohsYbLsNjWcKJAr2Z1SYNSgd3Zzz8pWeUy7zUDarWhmVrKecI+g1RckiCnEeQR+K9aANg2pvwRkSYnuJd3zRMKwuIsxMUGZlexOp6Ga8eGe8yvpMyCbmohfOhYvZikvBU6UIW5LCdNqHpqVYs8aQNlfDUygSbdszVm9jHRuK+CI/dlmeynn3tVfvoxIG+Dh0fI2glWTC5KkPoZBkNLqB0MXiqSmiOAOfQBrvO7Zf9EQRJk85PheXp17xcI2qWYiLFx81U6hO3158T+3tbyrb2x23Qz6TnOAP//A+IpGzZLNpm+dL11UeG3zaqosEEERnha6zd/d6dvX7Ud/6GELA8IZP4zn0t3hjBy1S8Ua34NONLE0+3xiJRJBjxzYaO0BCGoaKLvArGvWAN6kxdCFs3WomrTLYX0P/NR9FKB9BSAVNGF8x02cYa7sDpeNefAXjFge5jrXdCQ6eOK391hLv4KJFJ+nuvqzs4vfivrUAvOY1a8um1hWKZE/iIXSEY1xTvn36YY7U27uC0dEwUgjbPU9WujElzFDF98JA3EIP55RjvuaobMLF3ILLU+VgFtQ1+shkDIF0YSIKXVcZHNwPJBga8vPQE1vQ3/BzlOgIQgqa8OND4skonNTuQ/r+nGByn7Uu6tFNqLm+y/IUoGQ8RAcbLP/U0EiQ7JIzKIEkSMj2L6B//ScRivEIFGFwpuFiE8TabsfT8Tczw1MFa1w1Oz3TGdc0UfT2LTd4CuH4XGYMc52nctfNN65yjapZhonX25h9qFbfXuoBNCHIE56zdzCT0Uo8X3mDSrIQndqExs92XMXI1j1oi3tBQh0BwsIPbV8CQEHSCuj9UR5pb+P48Xp+c2JjwXTyYzcrUa5CcOrIMp7pqsez7kj+uH8UbXM3UoIPHz7hK6sVT7Z9gWRJq4FiWUMqusW2YJfzDt519y3WeTo6I3ocqejIjIcXeg0PqJRrS71aUiB1wRUyyzGhEK8yxm26YIU/5+R+w8MNnHrNv+EXfnyUBjNPFBOVYpiYTi9ftRmMyo1pzr8kM9ME5l8uENqFi6nC5SkocT5NgKcgzksvLaP9ZB3azQ+heHR8uodXKQtYTIahc3U8unstyo2P0+/7UzxSsFKJshqBFgvy0K5Xcfx4nSNPpY4vZyBckKRp2SkUbwaZ9qBfiKA0GFlhNakREOVjVS8mTzmiip0e51iumVnnhocbiG27f9r6m+s8BcY9lM0gWOU9zCeeqsqoEkJsA34LOA/8u5TyVMGxBcCPpJRvuDhTnJuYrBdv4vU2Lg4qzX+8e6tG316OlO2v8++V6FZ0K84H6uvPMDCwxNKRmwuu+aVUkKwky8i5Ov579xqUG3eg+JIIXbBICXEFKouQBP03l60LAs5b3tuyCr/61WZOLTyD9qYnbBXbjQsFHwq8j0hogMbGbrzeBKdPX87oaNSq1xGJ9NPY2E1z88t8cft70AmiMFpCCuVkDZW8g8a9GwVxx+QYaTWNZ8m5svdhDGDsvv3ikWt58xv3MFDBc7upgoxwMig2qEyERGjcWiFVYwJSjEopZS9ZatfpkJLMkDdzNsDlqYnD5amp8lQehpG2GyeeMrlGCMmuJ7ZwauFZtDc+iQAWywCtSpBGmaHjwBoOjoH2jocRCnzI//4J1yVSLz9emn1WgtCyeBqGQMJtgfdfMp6aCirFcs1JzHWegqlz1TziqXF/vQghbgYeAPZhxKd/WgjxXinlz3OneIHrp3NSQog64NvAjUA/cIeU8j8dzv048GmMaqU/Av5YSunkYJkRTM2LN7O7BeVQaf5AVfc23n0616nCyqBneMA20tr2Jfo6Pk889hxSSr56zzusKwr9V0S34udOVkqdQ/vX0JGQqO94BKGAT9dYogTZhODc8cU8+PwadF3kjaLCVOwVFoIf/Pg6sq9/ErXOICY/fjx4rOMe4SESGmDz5ifJZrVcwUedhoZuBgebqK8/a9UdOXPmMgTSVvfCDsPIKZQ1OAXo2q8SBEWQLFlkFX83Q9c9wQ8eegOLwzfi9/+C4r+1ROJGHrj/hnH7mQhsxqyZTVGR02dQFeBSpWudrAdyOvFKCVZ2eWricHlqajyV35nKG2P5OUnuKeCpPATxxJs43fYx1PohkIJ1IspyVIIpwSM7X83gukN4l58GCdrhK4zEPrYNslzsbKUfrP31ZJUsnnqDp7zCiybyuVoVoVxynposKsVyTStm+Ie+y1PzA9X8gvkssF1KuR1ACPEx4P8KId4vpXzgIs3rm0AKWARsBn4uhDgopTxceJIQ4i3A7cAbgDMYpPqlXNslw9S8eNXpvKcbdo9duTGL5z9VD6X9Hr/3PcmvfgWnToEnGGLV1av4s7s+wI0b1tAo05wa/T7DfJiQeAozs51S3F9sL2MHt/PomZ8xuO4QWo6YagmwTPHSqis8076R39T0o77958B7bcZUVbO+5UE8igRd4bbg/yi7GIXDgxZRAUipoOse/P44LS2d1rHe3mX8xZ/vQlF0dF2x/m1qOsHtd3zI6s/0yk00QLfQ2ItE+mlt3YvfP0oiEaSrq9WKRVt4/C4Cjb9nPVejb8PzNxreSP817wd+NqHnVAn/FsfoXcsiJfiFDx/lpZIWppngZiRd6wwHLc80ZgMZF8DlqQnC5alq4Ly+bNv2WElb6e5XUW9SEvQ/xqIz54gt+BRXK7U0oDM2EOSHT7Uh37QLNTSGyHhIvbgGueZl4KpSnpKVn7OsG0BRAF1wW/B9U+IpgK/c/V36+5ttPCWEpLHxNJ/57AeByfNUISrxlN1QM/vmosU1TWoNc3lq1mGmeaoao2od8F7zjZTym0KIc8B/CCE+ADw1nRMSQoSAdwMbpJQjwFNCiAeB91NKQh8Evm2SmBDiy8D3ypw3w5i8F2+66m1MBM5FEIs15JXmP9FFw07KBw7AO98Fa1vh3MZPs2P7T7njbV/mjc/ex0/3v4HhLfvwLPkwg/LDLBYhvO3vKTu/4dhzDF33JGpoDHJyv7WoLBj18pMdVzN27TNojQPjzkoISojM8BhKVKkRVAK2xahwARodjdLR8VoAvF6jFomUCh5PFr9/lGQyABjZoZqaTqDrHqsgpKLonDu3wj5u7h4nG6Ab6NjO0qXfRNNSJJMBW0aq0dFwLruUvWfjyUqCI8/T2HkP/W2frDDCJCEFYRGsandqPnmy5g1mFxm7PDVhuDw1PiZuPBbufrW3vzl3vWKYAsKYQzB2iDcqETzovPDCCnb3htDe8TCKR6KcryEdD6C1vVhhWoZ02+n7JxRDCh5WwlPmKYD+/mYaGrptPCWloL+/OT/uReYpu6FmIEsIhVG8sf1o7bdeuqQRObg8NQsxwzxVjVGVAOqA49ZkpPyRMPajv8v0E8MaICulPFrQdpDy0o31YCutfRBYJISol1I6/3K+6Ji8F2+6K85X01epx9KJeCodn9gfaDR6JbHYHkAiJdz7N0Z7IrqJ17Vt5g++cxlLG/4HX78vxZY7dqL4jZioVUoUPxojJXMQSGkUi1VCY3h0haVKmI0oXDjdyI8OrsTzll/g8aVBV6hVSiusC8gX9nXYwYqKaOUdFQzNvM83ipQKqZQ/16aTzXpIJIJoWsryAOq6iqJkyGZVEokg4XDMqmFiPmdT1jDZAF0tdoAFC3ro6VlutXk8GUKhC7S0mMRt/2wtVSSSYKyDpo5vMNr2+YrjTBiCcZ/lfEHxAm5p4916ItMFl6cmDJenxsPkjUeJsYkpc+VBineaJN+93/hT8Ww4hPeGAyBBP7oKfWk3ntoLiLRKctdrc+dX5qWSeU87TymWQWXylJRG+6XhKaBIkjiTqdXnK1yemjqqMar2Y8gWCgvYmITlAf5jmucUBi4UtV3A0MmPd675OgLYyEoI8UfAHwG0tCyclok6YapevOnKnlS9Zr5az17lIN3qkaGt7bMPK90kAAAgAElEQVQ89dTfI8UvEcKIn9Kjm1nXdheLSPPUvgZ0XSf85k6UwBp8uoeVSi2egp/75hysZAfC8JZFpI+lip8NUvDcnivoVMZQ3/4oQoBXajQqQZpz/diWaLOfCtKKaoyAbNbYedJztpEQOoqSJZEI0dXVannfQJJK+QgGUyQSPtJpP2Nj2fx8imQNkw3QFUjuuvuWcedd7kpzBEN2MccMoArSCbO9UuDvdMBp/HKe4xLMo+DdGYDLUxOEy1PjY3LGowRGOXt2gRUfXHKGEPhuzf9IFVmF1OFW1A0vIhSJMrCAdFLDe+MTwAcmLFOfXp4yVBWKott4yu+Pk82qs4injFFmIrX6tGKu8xS4XFWAaoyq+3AI8JVS/kAIoQAfmcY5jUBJee0oMFzFuebrknOllP8E/BPA1q1rL+onPd1evMnCWTP/bK7WhsiRTLHH0smDWQ6TubcUiYSXoy/txPfBf0cocAU1NOInTAYFuOOO+2hpW8Hlr1lNHX6alFABUUh80S0kc4krCglHj25mgwiwOKny0I6riW3Zh7akBySE8LNc+NmgCwb6ahFClmb/yxlU1S4QCxtP0tLSydGjW/F40sTjtSSTQQYHm/D74zlpHzk9ukpPj1GPqbGxG58vwdhYmLNnV1Bb24/fP8rYWJhjx9q4sK00DGQyAbqBju2OxySC2//qZ1y40Eg2a2Z8Oo+U0NjYbWnlYXwvoznWxQpOdkI1euly6VqLM0bORv24LTV64X0Wpll3PYgmXJ6aIOYGT5nyuXLfz4vNUwYmdk0GKRPs37+GjpROU+36fBHdgttIRNsIoKBKSAzUMjYSQtv4grFb1bkafXkXnroUim44+iaU/W+aeaq5+WW6u1dz7FibjaeOHLmSnp4VJenFZ4KnACKRARoazvCZz95mO388rnJ5avph3UPxfZpc9QriqXGNqlyQ7wNCiFullOWS8/9fYHmZ9sniKKAKIS6XUr6Ua9sEHC5z7uHcsR8UnNdzaSUVBmZHrY7ixaX0veEhjGDwe7FX7xqH+hzG+3KButXCSi+byzZRn6tLJIC/+tS/8tJLe7hj1x0oHoVa/JZBpSJZTZbTwX/kpcFPE1J3WYujjG5mY9sXSPfW8t+/Xod4syEbRBfUKCE2olI7HOBHj2+lpuHd7Hj8Vwh0JAqj0Q0MtH0CoSuElRBKURqMcohE+q0MSdmsB01LUlPTx+BgE1ndS3zUa1Q3d0h1eot3E3v3vaWk3WnhHGu7kw8u20QodAGvN0Eq5aevr5nh4QbHRSuvby9FOrqZzs6rbBmgamr6UZQsJ06ss89pHC/jJatyX41euopg30LSuqSpaZ0wu+KXZh1cnpocZidPlTtWaffp4vHUxJAildL5xY5rOb/hebSWbgbkJwkf+gbEDiBzPDUW3cCmti/iG4rw0JOb0V//JOqKwdxu1VrUDZ0IRSL666g98rfsfPxDeZ4KtzGw6eOgK0RmiKdA5RbvpqrrCM0ET6lqmnD4PKdOrS05vxJXuTx1keHy1ITyF39PCPE94H9JKUcBhBBLMWQVrcA90zEhKWVcCHE/sF0I8YcYWZXeBbymzOnfBb6Tm9dZ4HPAd6ZjHvMDlTx5hbrzkRJiMr16ztXjJ+tElYDO2JgX/EZwbEPH1zgVOwRIvvlN2LVL4xOf2suiyw9j6NLhav/brC+mWT/D672bSGSQ7fd9lpq2L9AmBZ0HV7N/2JOr8SFRdA8LlTBXIRg40cT9nUtpWvFGQiMHAbj90z9laGgRHk+G06dXGws/VOVZaW3dQ0/PcrJZlUzGSzJpSChqagYYPL94/EdRYcs80LGdz3/0LoaH6+yklEPhwlUpQ1Alr91Y250MDzdw9OhWWlo68ftHicejBAIjuWKV+WudvIymZ2rVqhWoatryJNbXn+Gr97xjVkgxyhbWnaFig7PdwzglzE7Jh8tTcw7V8BQF51jphC4iT5Wi8q5egnQawuG3596/sYCnvkUkMsjn7/wHBm78PYSAwWNLefb4wlxyCh0lFiZ1vhZt44sgIfvC5SxMfYwQhrFxSXkqN0657/qiRSf4yjbvjPNUIhGkp6cFn2+ManbEXJ6qDJenpg8TMaquAf4TOCCEeC9wGYbk4tcYnrfpxJ8A/wr0YmjO/1hKeVgI0QK8AKyTUnZJKR8RQtwL7CJf/+ML0zyXOQsnzXwpZAWPpTPhFUoIq/N46sAoJ0408WTnUny/fT8NHV+zss/9wz/Arl3w9a+naVh/G/3eP0eRAp9QkLpAUSSNjSctr1smozE01Ig/dgDt4N3sPPcg/Ws68W7qAgkB/DQrPjbrgt3PbOBYaAj1LTsIPv28dW8DA83U15/Neb6GrJ2jahaXe+69yern9k//lIGBJQBoWpJUymtJJ6Lt7ykrMXgg8WBZWUAkNMBXPn4HFy40Ulvbh6qmaW5+maNHt9LTs6KK55xHXt9u/3FS6M3r7VueT6/eeJJ1635d/QA5z1Rz8zFbtijzWcxUlfu5hsK/r7kqj5il83V5ao6hep4yUH7naTp5qhTjx31lOH9+gTETRbKwDE+Fw4+gHOpmaOWd7ElmjeLxgDy2nMzCPtTlp41U6i+sQd3wIsFnDlr3dil5ygmRSD9r1+7l3LkVl4Sn1qzZR3//Els/WULl5+/y1KTg8tTEUbVRJaV8XghxJfAt4BmMFewvpZT/33RPSko5CPxWmfYujKDfwravA1+f7jnMBzjXzCheiJ0X5vEJr7jgopMnL4OuJ3i6wLgRAgKxQwjg7/8eHnsMtm8XRKKSxKn9jC0aZn2kkbVhI5JWSlFSP8P0OMVie4mGlxA5IxgbaSPV9nmuQGPxmMZPd1zN6NXPoi3qB1l+Ac1ktNzuTPUoDMj96j03W60S+NIXfsC5cys4c+Yy/vgjz+Y9jPF6+5e8zHZ5a6tBVOa9mffa0tJZFVmVq+cB0tHbKQvm0NLSSTIZZGSkluJEFZU8ecXZovKjQk37LTOmXZ8InH6Q3OJ/54wuxDNSi+QVApen5h7KxXZVt3OVx/TxVHlMtKaXE08FYh0EDr7HWJufFsR5DQOv/oixW3UhSioWsnarXJ6qzFPZrEYqFaCQpxQqPxuXpyYPl6eqw0R2qsDw9F0PvAwsA64WQkSklOWCc13MAhSSRWG190JUyohUnWEmKa0fUujJ+zKQYM+e9RzzxtE2HwIJa4mSyPX5k5w8+C//snCMP+N//lET+u/30traTiRynlDoAqOj+ZhvVU3x1a/+Kz09yxgersfjyViL8chIPf/8oz48Nz6aS6UuCCnBstmJVDVNIhF0fA7l4BSQC1jeu8L+w+Eh9ux567j9+v2jDA7av5ommapqEr8/zpYtT/DCC68mHo+iqil03cMnW1cQj0dZu/YOvvil9wB2kWcs1kBNTT9dXa2MxOtti6T579GjW8lmPWQyXv7nH+6z5BHjefK6ulpZt+7XhEJphNBJpfIxcsa/5bXr1QQNVwrynTQUWb5PcG6fxBiXmoRmWYHemYLLU3MMxUbNRLlqenhqvIx+Tu1ZOjq2E4sdcOQpE39zz79w/PhGslnVxlXDI/X86+B/oy6LuTzl8pT9WpenZn5CU0DVRpUQ4vMYWvBvYtT8WImhU+/IVa3/1cWZoovpwmSzPdmLGZra9eo08MZYxk7T+fMRxGVnAVgugrTi4QBGfanHd4mCHoy5hUJXMjKyB4mR0lXTUni9CXTdQyJh1Jny+cbo6VlOQ8MZvF4j/qqQGDxvfwShgEeqhJQgIrcgFpKMqqbxeNIcO7Zx3GdYiLG2O6HI2ybM/4Rxz8eObbTkBh6PkS69OD1qcYafRCKIx5OxZTpS1TS67iEUiiGlIJkMkMmoNDScIZNRSSaDXH75fhQly6lTa0o+BxB0dl4zrj7bSLOrEwrFqK8/a8kjxktUYSB/jqJky7Tbd7yqDhquFPw6Sb30A4kHHfXqVhrbKS70tux9ubEmTV6T1YW/wgKHXZ6aH5gMV02dpyrBafdMcPDgZxkefg6EM0+ZOHt2laMRo4QSCF0hooQvOk+Zd+T3x9G0BJ2dV7s85fKUy1PTgInsVP0xcLOU8tHc+yNCiGuBu4AdkEvf9grBbEhFOxnM/BzLf5kWIDn78hKG068nrD1unWd+hXy+axge3p3LDmi0ZjIaiUQIvz9OJuMlk9FQlAwgGBvLk1ehREII8OPHJ/J/noUko2lJ0mkvx45ttPTaE4HZlze237ach8ND1NT0kU778PkSCKHnCiXmUbx4ma/37399rsK9oLe3Bb8/TmvrbrJZFa83wfDwAgYGFpPNaoyNhRFCJxgctryfxrV2fbpjQcaCOQhFEgrFbLEAHk8GqJwOF0zZYIB43PDOSll4r8bYt3/6p8RiDXR2XgNgBQ3X1vbaZCmFhHaL/522Z1OMi+rFmkUL/Vzz1l1CuDxVgLnKUzDTXFX5R5+TvDAQuJpYbE8ZngraeMpcRxOJYC65Ata5Jld5pRe/ks90O908NUapgWBkAhwkm9Wq5ikw1sBinhJCx++Ps3r1AZeniuDy1CsHEzGqNkop+wsbpJQZ4HYhxEPTO63ZjeqLFbowFswkp0830JvV8SzvAkDNePjVU5vxvu82lBf7CMQOoVjEv4VDhx5nWUte5lBff8byRvn9ccbGwni9CZqbX+LMmdV4vUnr3EKJREiEUMv8mZsk03n06ilv2ZdPBytQ1TSqmkJV00gJw8N1VfWXyRi/+2pqBshkNIaHF3DgwPU0Nx8jGh1g5coOvnrPzXzi4ztYvPg3ORmDoS2XUkGUKRRpevDMeiVmBqWurlab/t0kbPOcTEYjFd3irDHPkd3w8ALSaWPeqprissueLzl1eLieSOS8RQBm0LD5ueafXMH8L7Gnai56yl7hcHkqB5enJoLK3/NyO2eKso2O8//OZf6V1nlOPFVT04ek+Ee8nasCIkAxppOnoBxXGTtwqprC48lOiaeyWQ/9/c3U1Z1zeWqG4fLU7MFEElX0Vzj2y+mZztzARINWJ4O562Es1sBv4dndrXSKMdS3P4oQsFhqLE4FjYVV1elv+yTrqEcgWEyWhUj271ctTbkA7rnnZqQUIHQkglPb/g2v1Fh06O/40/d9E1VNW/UrCiUSkY6vVNRCT4dnpZx3zZRECAGKkmFwcDHJ5PhaeFOLHgwOE4vVk8l4Lc9hV9cVAAwNNQKg6wpC6Dai1nWP8ZyAws8iHd1sq1eSTAbQtBRr1uxjdDRsS4NbmGVJ6qJi0K75/KLttxZkb8qP+zf3/hPd3ZejKDqnTq0hkQhZBGAGDRejOgmHM+aaPtuUh5iYa/OfTXB5Kg+Xpyqh+rhiE8Z9pclmUzz5yy2cbjqNdu2TyHanRBAGT4meRjJCZ6ynDY8n48hVleJ2put7X8xV2axq8Ue1PGWu36qapKZmgNHRiGWogMtT1WKurfMuT1WHiSaqcAFUDlqdOuaqh7G01tWr6O2rw+tr5bJctiMlupkr275ARipQxlMFEikBBKOhTYTiB/JtufPHohsIE2Sl8LL2ii9x+vRqwuEhy6tlSiQikf4ZKfRXLqB44cIuurtXE49HyGQ0R6IqrA8RifRbBQ6FyLJ799vxesdIp32Ew0OoahqAWKwekKTTXsAspCwtz18qFeDuu75Ld/flttoh4fAgBw9eT2PjaWt8jydDa+seOjuvorV1r80zOBGZiVMw9Jkzl9HUdIJsVuXEiXWWdxOMoOE1a/blpBt2Yp0SqpVDTKF+xYTTy1YYq6SPWSTncDGX4fJUOZSryQjQ3v5mKhuHSZJJ+MnPryN5/S9R688jdEGc1xLmVxQ/17HoBjKH1+BZfRyPL8Ng9DOzjqsaGro5c2YViUSIdNpXFU89kPoJH6jfxubNTyKE5Omnb7akfJmMj2jUqGnt8tQ4cHlqXsI1qiYF56DV6cBMeBgvBuwkNMLevffg8+ZriihIiO3nhf13c7TrYbzv/Gmu3bjPBrLUpFR+vvMquOEJBlr+AtHxNQJDhxGKsUM1Fm0j23YnV6ISHArxwyc3MTxSXzZbUWvrntwr+3Oc7kJ/+YU6j9vv+BAAH/3Ibmpq+quqK9LY2G1pxKPRYcu4NL18mYyGENLauQqHhzh06DXU1fUAOrqukk4bdUd2734rI/F6q2+hSLZseYIzZ1bxz/+ytWBU4+/4i7nUuoODht69ru6c4Rks6MOEo0e1oB3g85/7EUeOXMXx45sQQicer7H1Y5JhKHQhN5MJprOdYvG+qXjTJhrMe8k8d7OzQK+LGYHLU+XgnGnQRDnjUAJpTp5sYTQSw1t/Ho8E5eG30HdlA0rvUK48iLEmjoU3clbch3rFEYQC4uxC0mqa4fjs4qrPfPYDAHzsT9oJhy9UXf/K5KpgsDChpgB0l6eK4fLU+JhHPOUaVZOAU9BqNRKC6uDsYZyOQoYzhWTSJCo7UYyM7CPz9odRtCweXWG1EmUlWdL9Uf67vQ3e9DhqaAyR9nBW/BPqa48gBGhSo1EEuRpB90vLePREPdo7HuE7SR2hK4SVEAp5iUFN+y1l5zXdhf7yC/X+kp8rTU0n2L//hrILhq1NkXi9Cat+hpGAIzff3GlSKihKhkQizIVtD/CZ9hTwUlnyeOvW3y7xIpnZmkrmAbnUtvbPSSKIbbvfdu54mZAKg6H7+pahqil8vlEUJVtWq9/btxzZI7iw7YGSY9YYDt6wSykxmCveuFeyDOOVDpenqsNEjEO9QAK1QHi4MBoCRdLf9kmkDkIBZbCW9JgPrfkISMgeWouy5jgeb5rvjH6PsBLEg8fW70xylaf9fXiI29obG7ur5inA4qpCngKDq1yeKpidy1NVYT7xlGtUTQKTTU1ePYo9jMVf1pmXWeTvtxB2TXQ+ja15rMwiI0EoOkLLUiN9rFSCrEby0uGV7OkLot38EEKRqEM1JM41om0wiCmEn2XCz4asoP1Xmzm18Czam54ADGMroASsrEnWWJbUQZa0XxwY95xXbAs+83d/zVibNs51Br6yzcvQUCNC6Bw7tjEX/KsihOTChXqEkCiKXnKdSRLjoaurlbq6cxQ/D/Np3P7pn9qCcTUtaWVAMlFNJiTTyDSyMmXRdYXh4boCTb2O3z/KihWHSaX8JZ5BG6bZgzVRHfh4nr7itMPV9OnCxUzA5SkTE+epwvMLX4+M+BFBwyCROqTSHoQvYfQkQD9yGfqy03gWDKGkVZJHV6GuN5yCqlQJ5sp6lBtJlIw5/VwV6NhuFcidLE8BRCKDDA/X2ngKQAhJLFY3T3jKcG6uWHEYrzdBJDJIwEmO6fKUixxco2qSuJgkUbk6fJ4gZkpmUSqPMFFpwcjVw8hNV0Au0YThWarFx3oRZElGsOuJKzm3/BjeG/aDhHXSx/MPvh3l1h8DEBZBNuNlwYifB3ZeSeq1T6HWD4GEoAiiUZ4MdIIlHjmzvRIKF7RIpJ/Gxm5rUf3yfZ8rWVRLvWIGJlqZ/cv3fc7q5xMf30EsVo/Xm0BKga4bns1qkl04obdvOaOjYcvYNL2FpvdwYGAJ9fVGHTFVTVtaeGBimZAwCOv48U0lc/D5RvH74yxadJJv/e9rrfZUh5G5qdib+cie8Z9hOQIqrqliYSI68CnU63Crz7uYDXB5ypiDMyods1ZydD1F+9MbOR4dQN16GICBjlbkTQ+jREeQErKnmvFcfhyhSMTAAtIpFW39UZClZT0KEejY7mg6VYrbuVQ8BfCVe9+Dhzif/MRjFk9BPgHFXOcpo+8kl1++n29+6zX5xhjQsR3A5SkXZeEaVbMQ41eHl9a/7e1vvugSi1J5RGXvi8y73XJX5ecLRgBvEwo1Q0F++OQW6lrezMreA4heYxn1RLcA77au34KCfnIR9x9uQX37I3i0DOgKkSK5XzFMj1y17RZyC9rCxpO2WhhDQ41lg4fzaWqr08M7e6Heyc6dnlwvAk1LkkwGcpIKQSajQoX7rQbDww0lUgmzzpYpuahUZHKqmZA0LUE67aO2tpfiYos4SDa09ltIV0qXW0RA5Wp/AWWr0xceL/HmKZIHUj+p7OW7GJhH+nIX8xdznadKUSyTvApIMDrq4aEd24gseSurhvchnpZIFEYjbQzUfByZ9iC0LGpLN0jQO1fD8i6UuhTogpASLFvWw0T5khzGbCr+UL9kPCXZudPgUKN2YpJUym/0JpV5wVNm/wsW9FD6vOwyf5enSttfyXCNqlkKe3V4MysRlBLFTEgsisd0lkyUGlT586VUGA1tYqDtz1mWFTz44HUs2nhNLsNffqxY7DkWNt/EgP/9ALx8YDVdUkd922M5GYVGsIzcrxh5Y86+KFarU29p6bSICvJp0otJyLFgodM4FbxQ5kyHhxewYcPT1jGfb4yn2m9B6vBA6ifjT34CC16hDMLnG7NlpSpGpUxIjiRM3hNnBiF/9Z532I+bhlW5a2FCmbCKM1UVYkLEc4k8eK4Uw8VcwVzlqfIwzzdlkncCaR588PUsWP06QsnnrGGE0AkNH4Rf30f/5j8DLQtZQfpwK+qGToQiUXSFsBKeAk9Vh0vDU0Z7PF7D+vXzj6cAVqw4zF13l8a6OX0uLk+5ANeomhMYX2ZxsSUW42WRclqsjf9fvvxXPH5wFZ437UL40vilQlMyRCarEowfzJ0lrKskOiH/DgbV/4HS22DUA2nqBwkBAniFt+x4xdKxIvvOgsQIDC6ksnIZffz+UZLJ0oKMJqGYi/OqVfvp72+2vHWalmLx4uNkMhrHT2zkgcSDJQt5IZHYvFc5yUModIEFC3pQFB0pBem0l4WNJ20FECthvAWvXNDw8eOb+M2JUq9fISpmQqrCE9fdfRlLlx4p2/d4xu5UMmEVPv/JyB2c7suFCxcG5ipPmeds2/ZYUZux2oyO+mlO7s+dZVZONGJ1g9lnEf6PQkYhc6YJbeOLIMGHD5/iK2tQzTRPgbH+rVph5ykwEk40NHS7PFXmdV3dmaruoRguT72y4RpVcwDjyyygMmFMDc5kKYEIMFx6kYXr2XkmYhX+jUgv14kAWk4eULjwmzFXZnv2hTXIVb9B8aXHlVGUy/ZjnycFx+z/muebXiYwvESmhMD0AOZ7y12ZW5zj8RqEkITDQ0ipkE57WbToJEePbs0TVMFCXmmxM+PAPJ6MJXMQwiigaBZAhKkV3nPKjBSJ9NvS2zqhmkxIUN4TVzx2IZx+XBTOc9LIPf9JEY2rO3fhYlzMZZ4aLyNipV0e/XwEtDRqyxmQgpCYZTwFoIsSnhJCJ532GYmCXJ4q2TGKtr+77PkuT7moBNeomiMwJROFBQrtuHhfpjxZlgsCHsYgrBEMiV8urSoCT/BKOl/+Cb7fMRa1JkLUiwC/6biTeGw/ay4vyJRXYFABSF1BLOlB+NN4pIeQEqooo3DSi9u9fHY5Yvl+8ou4KSEAchmCDPIoDh7u7VuOR0kRjQ6gqhmSST9Hj24tkSU4LXiF7R/58FP4fKP09rbw8sub8XoTLFt2BF33kEiE8vVMCshvYeNJWlo68ftH6e6+zDlDkXWP5Z9Va+se9ux527hzBECREwu8Je91dILAqENy4UID5qclhM7YWJj6+rN8+e5bHa+9mCgm/0ryERPF1edduHglYK7wVOF8ysd6Zejo+AKx2AHWrNWdeQoFGQ/jWXoWIRXConKc72ziqUxGJR6PEhtusJ0333gKStdspx04KNwdKw+Xp1xUgmtUzTFc/NojBsql4nVe5IfZtu0hIMV//deNZN/6C5QFF9jY3wwn04BR4LeeACMdX4TYc9aV1lde6La2kfhbQTUCTQNiIvFTxe3lXjsvIgJYtOgEPT0rGB0N09PTQjA4AkBDwylSDoGomYyPeLyWUGgIjydLS0snQNUyCBPNzcfQdcGKFS8gBJw7t5y//dqNtnPe4M8ad6ELVDVJIhHiyJEr0bQk9fVn+crH72D//m0MF5Gl6R0UyFxa2mbbYU1LTii96i3ed1UtN6i0QwWCz332fs6dW8GJE+sJh4fQtCRSKng8aS6//ADnzy9yzoQ1wwGzVT8f/zvLa+PdFLYu5jlmL0/tGKdHCaQ4cOCLjIw8Z5FGOZ4CSIzeCD6D4wLCX9GgMvqZPTylqhn8/jih0Nl5zVNA1TtwLk8VwOWpScE1quYYLn7tEWhvvwW7VEI6eP/y6Oj4Ahs2fLbEk2fCWkpi+wpa8tp0abUIotEtvPzyj/GI+0v6cYJTTSr76NUtXv/5/ctKKs+bSLGl7DWqmqSmpg9d95BOe9C0lE0GUS2yWQ+RyHljtrJ0zhJh8z5FowM5PbtCMhkgm1U5d24Fra172bvvLfZrTa02gqGhRVZaWmP+udS0F0k+YPc6lspzzp9fRDxeg66rxGIN+HyjhEJDpFIB0mkvp0+vdvRqXrSFf6pkN5G0uC5czCPMXp6qlChDAgnOnw8yPLwPoUC5HSUztikb3cj57h+C2FX1nGcTT2WzKoqiuzxVAJen8nB5anJwjao5iIuZlraj47NUjpEqj1hsL488ejWZa3+NWhsDCf5Mcf2oPJkYxpd9d8oTfRVb1mzn0Z1XobxxF0ooARKEGP/L7VSTajKwZ6Kzk6pTEKrfH8/V6DA8laa+vbGxe/zxCjxY5q0+//zrSCaDJJMBPvWXvyCb9ZBMBhkcbLJdq6oZK9uTiWxWxe93Thufjm7G48mgqmkyGa1iatpifHTZJkKhC3i9Cfz+ETIZjUymtP5KcYpYu4fWTlgSg6TXrHmOeDxKOu3P3btR62TPnrcCRRmRLqIXraqsVS5cuKiI2clTlRJl6ECWzs7liMLitVLadqzi4U0MbPo4vtPNJLY8h9owABIUMX4a8dnGU1IqZLNaVTxlG3sO8VQq5UdRMuh6+Z+7FleNy1OCwcFFtLR0kkiE8HjSxOO1DA4atbBcnnIBrlHlogiTzc4kpWTgNe2okThCF7xBCZLJGoZFQ8fXCMQOMcL619sAACAASURBVGIZVJTIKASgx57jyaf+ntgNf4gSSFjJKcaTVEAVtaesUQq9jU5ZlwSVApNNzfXrXvcDurtX09fXjKJIRkZqAIGmpRgYWAxAc/PLTORrpmkJdF0hmQwSCIwAgoaG00ipcPDg9SUyjcHBRQghURSdbNZDd/dqFEWnp6fFUTc+1nYnp0+vJhwewu8frZiathCBju3U1X2TBQt6yWQ0vN4E0egAFy40kkwGK6aIle3Fz9T+WYAgmQygKDo1NX1Wn4WYUmajaQrinUrgtQsXLqYHk88i6OzV7+j4HLHYXuzKCVlyRWjkIOLZ/01v66dQl46AFARFAA+ecUe/FDwlkFx11c/p7LzKxlNgZKrt6WmZME8pSpZEIkgiESIYNIzbxsZT6Lpn1vGUqqYZGFhCIhGy8RSUcpUzTxnZA+vqetC0FLruQdOSZbnK5alXNlyjyoVDxqZyKK9VlxKkECiROD7dw1ZlAY1keGkkwOLa3yUY67Cd6xQeJSUE/TtRgu9DSIVIFTU+8jMrDPW1eiz614BZoM9JP11Yvb1kjmBd86Xtv2NrzwcX51slghjVyRhVNWmrTJ/Nqui6YGwswthYuCyZZLOalflJSkE43Es67SeRCFUca3i4oUR2MR602AGyWdXahdN1BV33EAoNlRhAxcjLVMr/jZ09u5IzZ1ZZ9yrl9EoPSoJ4i+LBTIxLaK5UwoWLS4Kp8lT+mFPfRnIFM9lSJQQyu1FqRhC6QnicIvT20WeepwDuufcmq32qPAUGPwWDMWuny9z9isdrZx1PmWNOhafMZ93dvZpTp9bkeCqEEHJaucrlqbmPqZW9djHnkc+WNL4uNxq9mmj0mrLHxqIbCEgP25RalskM+/au5Tf9f0FI2wmYmZNwZCuJwPifxIePiKjeoLKuL9uePyYRtgDesbY7SUW3lD2eDzaVBf8VQhT8Vzqieb5j0GrhFQUL3alTa+jtXUYq5SORCBMIjOD3x+nqai1/bS41biAwjN8fZ+XKw8Tj0XHJYzIQSJuEwyzAmM3tSEpdOGYTcnrW5p2n0z58vgSqmkZKgeJmJHLhwkUO08VT5RJlFGYMzDv9zHWKoiXeNEkkmtSIKOGqDSqjv7nLU4Xo61tSwFMh/H4jSYZ0+HguJU+BkdyimKfKcVWlZy2QpFJ5njLuy+UqF3a4O1WvcORlFOMFyUas4ONijGRvYLDtNhpQWJASPLTjWrz17yDMHlvP5fsvPirw42eiyGvVy3v7nDDWdmdZ7blZvb248KBTZiABpKJbSs4vHHvRohO0tHRy9OhWS49dKEdYvfoA3/zWNgD+4s930dR0gmxWJZXyV5Q8FMYf7d33lrLeKLOtUO8tyOL1JlCULLruMeKjKF9YGYwnGw4PUVvbSzar0tq6Gyk9pNNeS08OgC7y4xTIDco9a639VuMHipYilfLj9SYQQkfXFatIZTmPG1QvcSjW1/f1NVvPxKnv6cJkPIOudMOFCzumg6ei0WtK4rw6Oj5j9W1XUYzPU0ExcYPAiad0Qoxs+w/H62YTT0ldcN8/GkZrIU+NjNTg8WQd7+FS8VQiEWTt2n2lPAV5rhqHp4x+BV5v0uIpwMZVLk/l8ArnKdeoesWjHHnYNcVm1ibnGiAGVCSP/uLVDLUdYEX3Xqs3p1ELfIEWJuoxA0NDPV3Bv4UoJiwtdqCivr3cYmwuPKqaZHh4AYcPv4ZEIojHk7G06Kl0gIWNJ/F6k9Z1yWSAoaGFqGoaISRXbv0Ffv8oe/bcSE1Nf8UaWOU03cVtqprk2mt/RjarWbrz8+cX8nf/8THGKDVCAx3bDX+nyOa8czrh8AVSKb8VODye3KDcIhyJ9LBlyxMsXnyc2to+Kxj56NGtxEcXVF7sq5A4lNPXNze/THv7u9B19aLLIVzphgsX04Hp46lCFEr+ShR5YuZ4ykN83LpNTphpngqFLljXmTwFRmY+XTe46vDha1m//hm6ulptXDVXeQpg0aJjNDe/bOMSk6tGRupKzrfg8tQrCq5R9YqHk/5csG3bY7aWUm8hgCSk/JJBbgNgZCSIqBlGdNuFDsWkNerbivClCMQOOXrMqkWlgrJOWZCqQbmK7nlUSbK5hWfLll2WpvzFF69BSli//hnSaS9dXa2sW/cs5883WpetXHmYkydbURQdkKTTfgYHVbzehFXocbyA3Urw++MWUYGhO89mVet5FRPLqlUrLELTdTWXHlcwNhaqvsZJmUV4JF7P/v030NjYzYkT6xgdjZBIhGwZBR1re1SxeJfT1wMEgzFGRupK5vNKJwQXLmYnps5T5ZNblPYpADNMZjS8ERRJ8CLzlHl8Mlw10zw1OLjIumzVqkMcP74BKRWjflSOqzyerFVWBCbPVbOFpwCrJlhvb0tuHh4bV7k85QJmmVElhKgDvg3cCPQDd0gp/9Ph3Nty5xauQ++QUv6/9u48uq3zvvP/+3svNhIgKXERtVKyJMuylsiyLC+SXe/xkjiJk7aztdP8ppnMdJ122mk8jZOmbtMkPadNf3NOk578mp7MpEsmTSzHq7yvcmxLtizLlmXZ1mZLFiWREhcQxHaf3x8XAAFiIQmAAEh+X+cwIYGLy4dX8P3gee5zv88z09zMWWVqizQWqTQ0rpKfu2W6io6FwcnkmwEirRvp2/g7dO7/q4ranvn9JebZl3puIt4iUyhMas8ThWzT/ntYuXIFS5a8T1PTEOFwKzBWZj6R8BIIjLB69T58vkimzC3AH33p18fd0uz+711feoB9+64lFguUnD4xEdtOZk7cacmkZ+x4jQuWJUvez6wv8jf/7w05R2Jg+47CiwdO0tBQZ/4CkIWMm1Ywmd9ZaH59IuEtOU1lojbUcgFH1Zg0q2qrGjlVrFNGKqfckupjW7o59d9rklOTeb6YWudUtv/xR19I/S7J/C8Id33pAXp7ezhzZmnBrJqJOQVoTqkJNVSnCvhbIAZ0A5cAD4nIPmPMW0W2/7kx5uqatW4WmtoijeNHC03mCtSyXZ/HIgh8FnALVwQH92OMk3uFKrSJvo2/T+f+v8qpCigYd7StjCkQpUrLFrsxeDKKvVKAge1upaR02VrfrjsxCA7NWIxk2uP3jxCNNhEIhAmFBhgeHturxxPnwIErUzf3CrFYgN/7b08DsHDhUe76n/9P3u/+5rfu4D9/4VUWLz7MC7vuzP1bC5QyL1Y9KJm0M+t/pNl2gi/90UPs3n1bZn9pBw5cyUUX7cnbTyXHN8cEI3pSJCSKhXM6yFau3MupUxfQ29uD3x9h1ao38HjiJJMTlz8uZC7PFVc5NKtqqNKcStu16yaghe3bdwDQ2nopg4OvktOhMjASuoS+jb9Xs5xKP1+ORsipwe330paVR9/81h0Ak84qzSnNqdmiYTpVIhIEPgdsMMYMAy+IyP3ArwJ31bVxs1ypRRoLl7FN3bCaNaXP/b8wixYt40P+nrMb/4Dgnu9gInsQcTAII60b6Nv4+xgDTYNvkv3K9H4nMwWiadyNuaUWVCxn7vtkFZp2Mb4dfn8Er3eUkZEQodAAzc1D+HwRIpEWzp1bwLlzC5g37zTgrjq/YMGHAPT29gCFg9i2E4yOTv0m6ewT+/DwPHbvvhnH8SDi0Nw8RCLh5b33Ch+vgYEObDuObSfIfi9U6/juGL0/E6zVnNrgVk0URBwikRaOHNnA2rWvTFjKV6liNKvqo9ycyjfErl13sn37DjZu/Cp79/4Fw8O7x3KqbQN9G38P49Qup2D6sqoWOQXVyyrNKc2pmaxhOlXAGiBpjDmU9dg+4NoSr9ksImeBfuCHwDeMMYlpbGNDmvwIXjn7LXzDb2rQKiP9rW33gbgns7YVX2f3C5vxf+ZB90lHSPZ2YHefLTpiN9EUiGIBkSSIRTinNtRElf8m+j3FpFuYv5p9gZs2jUUgMMLQUDvDwxAMDrJu3cucOLGKb//jb3HrlldZu3Y3LS3nUmt9SGaRRPdvyF83w7YTRUus5yl68reIx/2p0rCC46TXG/Hg8UQJBMI0Nw+TSHgy1Z9eeeU2RBx+8zd+nqlONDTU6Y7eNeh0A3e+u5P6Ow1DQ/N5/fVrc+7ZmjblHpMGPZYqQ7OqDPXIqeKGUv9vWLHiL9ix80oC/+5f3UcMmIgXqyk+TTk1QnpR4fSkuXLv03L/gmJFKVy1yCmoMKs0pwCDiINlJTWnZrBG6lSFgIFxjw0ALUW2fw7YABwD1gP/F0gA3yi0sYh8EfgiQE/Pgio0tzHkB4phcPBl9u//csWBVeyGX8exEMvJf8aAWGDNGwQDgey50Abe+udBHv/ptzm69yjnT8IffQluHVfhdKLL9PkB4b7KYoTB1JSOcuWOLBYXb92casHEo6JdXR9w9uxShobmZ+aXj46GALh1i3sT76uv3sy2bffj8cSxLHcOtTHuX+gdfD0viC0rSU/PQSD/BuBSZU7Hzz83jpU5aYdH5gPuAsTB4CDGCMmke6NvW9sZ+vsXZsrhvnPIvY9hQdcx1q7dw9DQfP72u9sm/mBQwUk4p9Ru1pz17DK/JX5x5u9MJn38y+CTmeOU97oqBkK5UzB06kbDm7as0pyaumI5Vby4RdoI/f0tPPL8Rnx3PJR5NPlhN/aS3tReCk/ZqyynJr/IbiHjr4BNpBY51brrs8RbL0mVa9+baVWp6WtFs4rxVQDnSk4Jxtgkk7bm1AxWs06ViDxD8ZG8XcDvAK3jHm9lbEgphzHmcNaP+0XkHuB/UKRTZYz5HvA9gC1bLpo1XempVTqaquKFKcaPjqWnAhpAHOEmK0gsOdapMkCi4zBL1y/l+l+5nu98/m9Sj06yOlH6d5c5cjiR/JHF4tIn5LHALf67//jLv5b5/rd+80X6+xfS1naWnp6DBAIj7N17HaOjQQ4cuJKenoOZdSqOHNnIf/7Ca9h2AttO0N/fTXt7L/Pn9zI6GqStra9wZaUKy5wGAmGMkcy6UeD+e7vz2sdGzRZ0HWPNmldJJr3E4/5J3Wsw4Ul4ElMqqjXtQgNBFVPPrNKcKsdUClOMefvtZbx8KoT3k49g2QbjuM3zLOt1BwUJFLz6Ao2UU8X3N3Zdqj459edfvxOQ4hUAK8gqzSnVqGrWqTLGXFfq+dQ8dY+IXGiMeTf18Cag2I2/eb+C0p+FZ6nyAmW8QlMzio30GWORlAAeRlI/kznyDkHWW210AoeGm5CQu9K6CGy65RK237YdG5vvfuG7JAILMHwwYXWi3L+qvJHDYrJH/caM/7vdfd/95Xs5dWoFhw9vAtw1lpYufY+lSw/xP//48xO2+9133StcweAg77xzGcZYRCKhVNl0d0HEy7Y8SizWRDTazMKFRwgERvB44ixffoCRkVZOnFhNR8dJent7sO0EweAAprd0ZaWpLBxo2+4Ci6tW7ct5fHCwnYMHr8jsq6fnYE6p2/Qxy77XYPyI6kT/vukAmUy1pDsDnwK0tKyqPs2q6VD7nHIfD1G4r9vGK/0BfNfvAgOJt9biWX/QzTJHCFrNePAUXVy3cXLK3Wv2z3d96QHOn19AIuHl8OFNdcspEObNO01v7/KCOQW552/NKTUbNMz0P2NMWETuBe4RkS/gVlT6NLCt0PYichvwmjGmV0TWAl8B/rVmDW4YpQJlcopNzXBnswzl7T8cvp2+m3+ZJS/9V7zpjhXuavHLt/8jQ/vv4cXB1zE4rGoSIvs3cHbDH9BqtSBZ7Yov+zSD229gKsodOSxk/KjfmPHH0/15YKCDcLgtc8IeDnfQ3z+M31/8luVY1n1diYSf9vaTqbUz3PLpsViAeNyXCZ2hofnE4358vgih0GBqDrkHny9CU1MYjyeWqayUbtcNNyYnXAujpeUsXV0nWLLkfUZHmzl+fG3BdTs8njiRiJ++vkWZx2w7QVvbmZztAgG3WtR46dAvNKJaaoSw1FSQgjSkVJ1oVpWj9jmVvmdr1647ye1YBTl79ggLF95C86497qIfjkVk/3rObvwDWiU3pwotljuR2uXU+CmPMDQ0n+7uYxw6tAWxTN1yCtJVACefUwu6jjEw0MnWrY/N6JzKdKI0p+achulUpfwm8A/AaaAP+I10iVoR6QEOAOuMMceBG4EfiEgI6AX+EfiLurS6jqa2fkdhxeekD9PaesW4kcEtvPPOA/j5ISe3f4d1b2/k9dNt+K59gU48nH/9Lxgefi2rKqCheXA/nW/+FfGNxYs/TFa5I4eF5M57n8woWf4J8vSZ5VkrzOcev/Tq9dk8nkTOehSh0HnC4Rb+/vubGdi+g9Zdn0Uw/N5/exrHsTDGcqdbGhtjyAtGgxQ8cVtWAstKEgwOsn79iwwMdNLWdpaTJ1di2wna20/R17cob+HCWMzHZZc9xtf+9JcBuPvLOzh3rpuDB7fmbJcuXTt+DZH0SGz62N71pQfp61sMuKGXHj3t7j7KP/9oVebfsCXYy3C4I+d3TCR33v0kw2uO30Srqkazagpqn1NjRTC259xrGweiPPnkv8Wf2J15VMRxc2r/TMup/POZ3z/KqVPLc6bbzZSc8vtHefXVGzWnNKdmrIbqVBlj+oHPFHnuOO61/PTPfwj8YY2a1rCmtn5HMcWnZuTuJ0Iyadi9u/DWHmBo6FXEgvEn7qbBN4lPoUWllDNyWMjk57e7f0s87qen52De3PAzZ5akvpt4VDKR8OSMAAJY1tgNx+kRTstKpCosOYg4jIy04PePYFkTl4q1rAQ+3yjJpIdk0iYYHGRkJJSZ455ua/ZaH2lDQ53ulI/U9JVTp1YQDrdlgiQtXWYXDBdc8GamTQ7NqSPm/tzXt5iOjo8yr/P7I4TDbbS3n8psIxiWLn2P/v7h/Hn31TBuMUalKqVZNTW1zalS3ClsPt8TqZ9TZyERwJkVOTUy0sK8eWfznm20nBJx8nLKspIsWnSY7/7dVZm2zpmcAs2qWaChOlWqPJWXpa18akbmFeIUfrzCG3SnQ+6894mPQTLpIRAYydtiaKgzVfUod1QSyIzoGQSPZ4TBwY5M1SLHsbAsB683mtk+PcLpOF5sO0Ey6WVkpIV43J96jTfT9mIjn5aVJJn04Dg2iYSPM2eWsmDB8QLtbi84j723d0WmQtXhw5sy22Rv6zge/td3bs5b78QmzH9dtolgcC8eT5wTJ1Zz+rS7lkkgEGbFirfo6TnIwEBn3rEt1GEdzzgy4QKMuQdDQ0qpRtAYOZU6l+Xl1NgH50YzW3Mq3aHKziljYNmyQznbzcScAjSn5ijtVKmqTM1IS08DyHu8Ae/LLjbvPTt4spVayHD8qGShudoXXriX/v6FAJmqSqOjzRw8eFlO6EQ2fpXe3h7icV9qFM+Tqq40n/7+hQxMUDq+vb2XZNJDNBqgu/s4f/3tGwtul66aVC6LdHDnXpUMBgcIh9tYs+ZVTp1agdcbQ8QhFgtw/Pha1qx5jf7+3FNPsQ8CkFuOdkfsZ0D+TcKFtlFKzR7VyanUeUJzCqhvTrW1nS2RU2P/Do2aU+Mr/I2f5pdeKDib5tTsp52qWaacKRaTm5oRJ5l0eP6FS7CveBmxHcSAZE0PAIhEPk5zcCeFphhEhiOceu8UAI7jcOb4GY68foRQe4iunq5K//QpKznvPRM27t/xrW9+n76+xRw5siHnROrxRNm85Snadt2Z8/pC65TMn99LNNrE4cObcm++LTAK93cf7JtyVaK0kRG3UlMi4aOj4+S4Z8d+1/HjayuqSlRsVNfnG+XI0Y8B4DgW4XAriYRbgWlgoItwuBXH8XDXlx7IFN0o9UGgIF14UKkZa/pyqpRRIMk77ywj7PwCIesZqlFIYrppTmlOqZlDO1WzyGQXWJxaMBkgSjjs4aEnthO9+gW8HefBwNXSzICTu7hfX9+9mM7tNEfyT7LvP/smX7n5K5ltf3TPj/jRPT/i+l+9nt/9/u9mHi/3JD2RYvstNO89svGr2Lt+JTNt4KOPVrJ+/YsYIxw6tIXTZ5azoOsY7e2n+NZffgLIrRxU6ETu3tPEhCN42W1It63Uor7d3Ucz64bEYgHC4dZxa4W40rWi0n97pau1FyobfPeXd3D06HqMIwWrNgHE4wECgTDnzy8gHZ4LFx5l797rCgZQ9mN5ZWzrOG2i1L+JTuVQqrDpyamJhEkkhGefu5QTiz7Ac+3nkf19NA++WXbOaE6NtUFzKr8IRU5WaU7NGdqpmkUms8Di1Fa2N8AIZ8608fCujXhufwTbl8DrWFxqzWMZDv1JC0mtro4Bx4Ezq+7GahsiSBBP1ltsw7Ub2BErfaKeaonTySpnv9nTBsLhtsxaHOk51YXmXKfXwKj2OiXFFkoMBfv4lx+tzHs8STDT/ko+MFxzzY85cWI1Z84sYWioc2wDy+RNS7n7yzt4/fXrJhzJS694n67IZBD++NvfILLRW/Q1d/o+XdGixtOiwoWWlZqLqp9TExkmFvNw3wNXE73uWTwd5yBpcXrh17E39uPFSzNTuPqA5lRRNc4pcI/Z1q0+hobaicUCuVmlOaU5VWPaqZpVildHSpvayvYOYDhw4AJk5RHEmyBkPGyx5tFNgrffXs7e/gDeX9gFQN/7PSSveQFP2xAYsMQqsM/SCk1HSAdAJZWUytlvobBJJLyZOdWBwEjenOv062Ktm6u2TkkpXV0nMr91jMFiJHMT71R5PNFMsP/pPb+UtdfcEcTx01JOnVqRmv8+8b97NNrM4cObuOF6R0fMlJpTqp1TEzt5sotwUwR/xzksA/4nbmR43Vup3zD1D5eaU1MzHTkFY53Q9FW4bJpTqh60UzWrTKY60sSBlveMSe1XoBsvCxIOTz+7hY+WHsV3/T73CtWhlUSXnsRuGs2sSG9N4qSV39LC7ai0KlM5+y00iufxxEkmbS7b8ihNTUOEQucZ626Mva6a65SU4vONFnx8sser0AhWT8/BzF5cJuen8aOnEdxwAwgGBxkdDRKLBXLK8ZbkiDtVYgaE1vipFMVuVFZKFTM9OVWKMWPn8pBYJEfHFoS1sYu9rCjNqamZjpwyjpRcw0tzSnOqHrRTNYtMrjpSZWVpm4zDgw9cw/AVL+NZfApxhNiuK/BseyVVvMKixQqVNfoHhQMi/Xglytnv+GkDHk8cv38E91gJ4XArIsnMnrJfB+5c81vLmM9caE49fLrgtsUqI1VyvIoFoGuso5UePU2PFqaDvKWlH8tKMjDQlZlCMZHxlZQaVtZUihnRXqUaTC1yakwy7xFjhJGoFwmGC2w/OZpT9c8pmKhT5r6HNKdmQHtnkalfSlANa+PGr9PaegXZ1xRaW6/ImYM+Flwm62vyZWmNEc6dm4fVfRqA1e9fhHE8iO2AgRYpv0MF2dMOcttX6XSEcvYb2fhVYq2bMQiLF7/PqVPLOXFiFSdOrKK3t4dTpy4gGBzMbG8QYq2bc0f5Uie38V/F1q9In/izFxz0De6lpSV/MUcYv6DjFI9XKiDGf7W09E/8WsYCLT1amEy6YzTGWDiOTTB4flL7UUrNHbXIKdcoyeQoZtzn7qGhJpK3PIbVPgCGnPt+J0tzqv45RdZixKVoTqla0itVs8z0l6XN5U1k3bQp5c1Pz1bt6Qi5o2mZZk56v+lpA1/+m7H9jMn9W8udG559qX7lyhWpkTQPHR0nU6VcDWvX7s5bVR5gKNxRcEHHYn9X0UpAWQ4e3Mpv/eaLJJMe5s3rzZSTHS8daNnHxO+PEI26U2tsO2uUOBXQ6ZE+v3+EYPA8Hk+CkZEQweBg0SpM6deXKk07XZW4piLTPp1eoVRJ05tTDjBCOOznkSev5HN3vJDzrGkZxhIQxyJkBcuapt6oOfXfn4Sm/f8pp9jF2N5csyWncITPfKqXpUvfy6sgmE1zKpfm1PTSTtUslhtKaW44bd/+eL2aNaFi5WOnKr+SkitvlG6K+xkzNke9omkMWZfqlyx5P3Oy7+tbnNnkW3/5iaIlbiNM7ng17b+HlStW0Ne3GNuOEw7Py5v2IJZhONxBf/8wPT0HGRzszJuJn5YeZUxPWclea8S2E5n1PtJTSNIlZj2eKG1tZ3Acm2TSg2U5rFnzKn19i/JLpsOEc9iz/33u+tIDnD/fjW0n+PDD1TlVoBp9HrxSc1H1cyoGwCOPXE3kmudAxt1rI+A1XpqspooGARstpwrta0xqKtgMyCmAlmAfxlg0Nw+TSHjysirdjnRWpduSK/dqmOaUqgXtVM0S40f1IAQMFdiyktK0pZVzw+90qlaFplI3w1Zr2kfa6GgzTU3D+HzR1Dz0KoQh+fPJvV43MMbPJ0+PYvX2rsiMyN14Y5Kduy8rOsKWntP/zW99Mud35n0oSI3ieXxxRkeDmRuEfb5Rkkmv2zbHl9MOYOwG4dQ+xodO9r9PX98SOjo+wuOJEwqdZ8+rt+Tvb5roHHalSqtNTjkAjIz4sYJhIL8EdrNMrYT6dKpmJcGxfRXW6DkFblYtXfq3nDixOtOZGZ9V2efYdE7dUIecymlLdk6l9pOdVZpTc4N2qmaBQmt6FA6qMeWWpp1JqlWhKX/K39jP1b6Ef/58Jx0dH+E4Fo4zNi3FmeI6KuMVnk8OweD5iW/SdaTkqOxkp8KkA+avb0xw8uSqnOd6e3vcKRmxYMHfUSoIJiopXFUz5UZlpRqM5lRh1awkOPaa/AHAcq58FTNdOQVuViWTnsx+J51VDZRTkJ9VmlNzg3aqZoFia3qUNpnnRzl+vJvjERv74ncA8M+g2ibVqtCUvx83sAwy8fz0CeZYjzdv3llGRlrweqNYljO2OZWdeAseB2Ph8SQq2m/aVKbCfO2eX061J/f9+oVf38tg9gLDk1SspPBEizuWI3vksdhCj0qpfNOTU+NFcRyHl19Zh6zfzyeCFsNDzbzyxko81zw/xX3VRjUrCebua6xSa6Z8ejENklPgZlV68C+tWlmlOaWmm3aqZoVy/oMpdcJOYMwoP39pHe/6h/Hc+jgisCThW0S6ggAAIABJREFUY6X4ebHcZk6g2jdxji81m/14rfYz1fnRgcAIo6NBRkeDRCJjo2GCoSm13sZkjL/Rd+VKd+rf6dPLxvYpDolE+aeAojcTTzAvvNjxHKsQNTWFSgrbdpz33/9YWftTSk2HaudUNgeIEIl42fnkNuzLX+HfL4jxwdGlPHtwKZ7bH8HyJjEOlLEmfY5GzalK9lVJTg0Nzc88Lhhad3120sekUIasXLmXs2dzs6AeWaU5pcoxcy47qBKmPqJVvDRtDMeJcv8DV/P+siN4L30DARKvbWKrHUAcG8QhfWJIJC3Eyl8LZKqKlWhNL9ZXjuxSs1CklGwN91NUVsnYEydWcf58FwMDHaxYcSBnsykdj3ElcsPhNrq7jyFiEDFEIiHOn19Af/+i8ts9xTK82cy472Otm8du1p2i7H8frzdKPO7j0KEtnD6zvKz9KaWmQzVzKptb7W9goJmfPLSNeTc/zR1dcfa9tpZnT7ThueUJxJsk+eEixKqsQm0j51S195WnQE719S1i4cIjOZtN6ZgUyJBwuA0RB8tyCIdbOH++i/7+hXXJKs0pNVV6pWoWKLaYIrQAw+Men6g0bZxz51rpi/gJLP0IgK7dW1mx6gPigyHuf+ZSfHf+DLENdsLmzb5mvNe8BJS33kdaNW/WzVatCk3V2k8h2SNlxas3uco9HukTd2fnCf7+++6J/TOf6mU43EHmJuMiq65Xc252ob8vs/cK5oGn/30OHrq86Ihk1U1xyoxSc1l1cyqbO/3s8OGlOEtOssFvg4G33lqFfefPEIFl/V28/8ZG7FSmlavRc6ra+8o2+ZzKXXR3qk6fWU4o2Md993cjqel24XBbKsOM5tRUaU7VlHaqZoFqrz1ljFt2Ftw3yLb1x+jt7eS+9xZmplFYw0GiZ9rxXftzjAGf8eMXf9l/QzVv1p3J0jfTFl5nxFR0PE6fWY7plUzJ26GhzqJzrXNOwlU8+Zb6UJIzD3zcdI3Jrq1Ry3K0WvpWqcmrdk6NZ7JvfxEwZuya1LxIqCq/Q3PKNb7ow5isDk8Fx2RoqDNzv/Lhw5saNqcgN6sm2x7NqdlLO1WzRLXLo2cYOHlyAbuOLsB781OIgHWkh3gggueCD8ARglYT3nFla6c677yaN+vOdJGNX8W767MFbpKd3uORXWFvR+xn0/M7SnwoKfceLaXUzDA9OTXxh+mhoWakbSDvcc2p8qWvurTWOKs0p1Qj006VKk3gxIkFWN29iMByx8c7T1+P/9d+CEDQas6b9pe/mKE7x5oShRaqebNuI6j0ZuZyj0fRUTNoiApAJT+UZC0umfOcloZVSuUxQJxoNIl/gkkS73rDeLa9BYAfd2PNqeoU3SjnmGhOqdlKC1WoKQmNe8tYBd5CuZfO01+lFyac9mIQNVSNm5nLPh5FTviQOulXM7CyblzO/ir1O8aC1mR9zdwPJUqpejBAhLNnAzz07MTnDu+qowjQRFOmU6U5VZ2iG2Udk1rmFEw5qzSnVLn0SpUap/KTWbnzzqezGEQtVetm5lu37il7msH4wCo4VaLCG1jLmeqQvwCjyze4l5Ur92XdkKyUUsWMkkgIDz54NZt/ZQfRaIgjHyzAuurnNBlBZNx5zYGQFcLGzjykOVWdnHKvOn06/4kGySmYelZpTqlyaadKVd1cn3derZuZW4J9rF27J7UmSDPHj691i01MpmR5gW3u9H06J+jqNf87/aFk/PQbjyfOmjWvAmhgKaVKMMTjHm6++WW8/d38+KWLkY8/hh2IYtGa9THY5bf8OR0qdw+aU1N5vChH6O4+Sk/PwZys6u1dMeFLNafUbKPT/1TVzfVL58VCeSph3bT/HpYufQ+vN0Y02oTXG2PNmldZ0HWs7HZNdg2pWhk//SaZ9JBMeunpOVjPZimlGp7B74/R39/GwwcXY31yJ1ZTlHnGi1+sSa1FpTlVeU4BtLScZc2aV/OyqqXlbFnt0pxSM1lDdapE5LdFZI+IREXkB5PY/vdF5JSIDIjIP4hUUNN7zjNAFMepfE+zad55OaoR1t7B10kmPSQSblXFRMI7607k40dEOzpO0tvbw9DQ/Enfo6VUPWhW1VMMx3GvVO3esw7v9p8jlmE1Qa625mGZVHn1LIU6WZpT1elUdnWdIJn05mVVV9eJ6jW2jjSn1FQ02vS/k8CfA7cATaU2FJFbgLuAG1Kv2wH8aeoxNSUGGOHUqfkEW8JV2eNsmXdejvz52FOvqiQYksnc/zwTCS+BwEjpF1awMGGtjZ9+881v3QGp45Veo0SpBqVZVXNucYpz55qJJQO0BqOAILZ7DtmIHxMR+iMh2ucP57yy2JUrzanKq//5fKOZDlVaIuHF5xst/iLNKTVLNVSnyhhzL4CIXAYsnWDzXwO+b4x5K/WaPwP+CQ2qKXKAEQ4dWsaLxzr53E17IBIovrUREIdqFLSYzSoNa4Ng24mcxzyeOKOjzSVfl55/fqfv0w1RmraU2VaeWM0dmlW1ZoAwH37YxROvr+KOT7xAMtwCdhKA6wkSPd/M/U9s4fbPPoUxHvdqVep5VVg1OpWxWACPJ57TsfJ44sRixT9HaE6p2aqhOlVTtB7ILhOzD+gWkQ5jTF+d2jQDuaGzf/+F2NteGL9+X563312K77P3uaODhvwqSzNQNdbqqLZ46yUsXHiUU6dWkEx6sO0Etp3gww9XN8Q0g2osgFitkVKlGpxmVcXcnDpw4AJuuG4Pcm4+P3t+I77P7aAZYRFeXju8mM1X7aNptIl7n7gc6/ZHsZqiGAOWNNSdDmVpxJwCCIfbOHduQepeo7GsCofb6t00oPKs0pxSUzGTO1UhIHuJ9PT3LUBeUInIF4EvAvT0LJj2xs00Jn9B9MLbrXkXywJxLEJWcFI3BDeychaArIXIxq/yx9+u4ERehTK0JVVpAcS5PP1GzRmTzirNqdKuueY1jhxZylO9QbyffATLNoQcL1hw0UVHOXeulZ8+fQn2LY9i++OYpDs9UHNq+vzdB/vK7/BNd05BVbJKc0pNVs06VSLyDHBtkad3GWOunuIuh4HWrJ/T3w8V2tgY8z3gewBbtlxU/6H+BuQTQzMWp86FkLbzBbcRC7zGS5PVVJWgqvfoW7XW6pgOlZzIa1GGdqbMiVdqKuqZVZpTxbiH4r33lvNqfwDf9bvAwAUmyMVWEIhx7lwbj+9dje8TjyAWJD9YjNXdW5UpgJpTpZWbVdOdU3cGPgVoVqnaqVmnyhhzXZV3+RawCfhx6udNQK9OpyhPe/sA2zvj7HllHQclgvfjTwKwEh/Zt/xaWDRL6ft6JqsRRt+qtlZHBaoxlW6mqveHFaXG06xqRG5Z2hMnurBWHQJgvni5mFas1NTA06fbsbr6EAucvvlY3acRXxIxUtH0P80p14zMqSp1pjSn1GQ11ERjEfGISACwAVtEAiJSrOP3f4BfF5F1IjIfuBv4QY2aOou4J+Vt295g585tHFpyBO/W18EInVhcRm4HqprTKMav/5AehRt7fPpVa62OiqSmJ4z/aqS1OqZD+sNK+oNB+sNK0/576twypUrTrKo/X4mPL9J+DvEl8BgvLdJSUW5pTqVoTgGaU6q0hupU4YZNBLcq0q+kvr8bQER6RGRYRHoAjDE7gb8EngaOpb7+pB6NnrkMEAPg6acvY+Cit7EXn8JOWiQev4F1BBgaauLkuRasFeUvOltMI4y+zfUFIOupET6sKFUmzaqaMUAi71HJeT5fE00EpbnigUDNqblNc0pNRUMVqjDGfA34WpHnjuPe8Jv92F8Dfz3tDZuVkhgT4cMPu1i27AzhcBMSCoPAupF5LL3ybQaO9nDv20vxfPJBLG8SY8AWu2otyF3/wWQ97o4O1eLyulb2mT4TTRdphA8rSpVDs6pW3CU/TpzoYMmS3NmSrbglvAt1mUTAh68qLdCcmqVSxSs0p1Q1NVSnStVKnGQyzmOPX8G8LftYNu7ZluZRDr6yjkO+ITy3PoEIJPvbsNsHqjr9r9j6DwI1nbOulX3KM2FFpQmqLo1fVDHz/Ayv1KWUqoY4xkR57rnNxJcfYcm4Z+0aTbTRnJrZimVQ5j4wzSlVRY02/U/VRJRjxxbSawx2V3/esyORJt4+uhjvln2IwLwDF2M+nGh9y6mLbPwqsdbNBU5Xenm94aVK4Y7/mkopXJ3SopQqLsrp0+30hn1ctaKPgwd76PWOYK88CoAfCzAEiE9rKzSnZjDNKVVjeqVqjkombfDkz1MHcBxBUmVoPUDz6UXQ3jst7Yhs/Cq+XXemfsod+Zkzl9drsVZHlVWj2pNOaVFKlWLbcT5z026ef3orJxZ9gPfGN8DActPMUvESNKO8c7CHdRcfz3ldtdel0pxCc0pzSk2CdqrmnNQoS9wGO4G3AS5hz/XL67UsR9topWGzp7TMyJK9Sqlp4ObBvHkj3HvftUSvexZPZz9ihC0yj8V4aDZxHnv0Spz1b7GuJi3SnKoVzSk1U+n0vzklCYQ5cGAFr/QG8V77PPOoXuGJcunl9dpo+NKwc7Rkr1IqmwOESSaF06fnE/YksDv7wcD10sUCAvhJEB31cfKjLuxlJ2rSKs2p2tCcUjOZdqrmjDiJRJTHntjCHs8Qvut3IZahxdT/YuXYnPWxG0NjrZv18nqV1bw0bBXmsyul5pIEjjPCC7s2EjdgzNgHVZ8IgZxBwNp+iNWcqg3NKTWT1f8TtaqBGBDj4YevYWDzq3iWncQywjZpZz5AakX6etLKRtOv1qVhdSqEUmryEsAoTz+9lROLjnGZJzeXGuE6gObU9NOcUjOZXqmaExwA+vrasLrctT6ukQ66CdSzUarGis39nyv3BCilGpmbU6dPz8daND2FkVTj05xSM5leqaqj/fu/zODgK7hzs4XW1svZuPHrNfndfu1PzznF1lupxz0BxW72NUXWDFFK1U89s0rNLZpTaibTT9Z14obUy4ydOAyDgy+zf/+X69ksNYs11D0BBW72TdO57Uo1jnpk1RIP2EmLtw/2YK89CFS/TLpqTJpTaibTK1V14o76Qe5McZP1uFLV18j3BEjqhuEdsZ/VuylKqZRaZ9Wllx5gMU3sePAyolfvwtNxDjHCRmmblt+nGo/mlJqptFNVN8VGNXS0QymlVKOoVVa5+wuFoux4dhP27TuxvQkCjs2VVjvN+nFFKdXgdPpf3RSbylDtKQ5xkkmHJ5/ejH3Vy0ggCgZ8WAgJxJSu/Oc4FkdjBnudTsFQSqm5pxZZlcBx4gDs2rUJuWIP4k0wz3i5zuoq2qEKhcLcQIgPP+zkzY/a8Gzem2qZ5pRSqva0U1Unra2Xp77LXUhw7PFKGSDC0JDFTx7axkebXsOz9l0kVUq9iSQmluTNN1eW3EvSgPfmpxFPEq/x4sNXpfYppZRqdNObVQaIEok4HHhnOQDJpIV4EgCskRasIh0ky4rzi597ir271/HkRy3Ytz+G+OPgCEGCVWibUkpNjXaq6mTjxq/T2noFY6N9QmvrFVWqqGSAEc6eDfHTxy8jcdtOrPYBmh2b26SbpQjxEYuf3PcLRC48VHJPYjsI0EQTzdKsI4CqOnTBRaVmhOnNqlEGB7385MHtxFa/N8nXGCwMXq/Do49excGOU3i3uleoPHhos1qx9KONqgbNKTVFOkm5jqavJK07mvj++0uQnhOIN0Gb8XCDtQALwWaUEycWE583gARiTPQ2CEkIO2cle6UqowsuKjVzTE9WGSDJ8eMLSS44g3gTgHfC1wSJIg5Eoz4+OtVJ4LajAATET0DXXlRVpDmlpkqHc2a1satKHeLPn0Yhkxtt0Q6VUkqperJI0mJGObBvJfGkMP6eLp2arpSqN+1UzUpJHEc4c7YVq+Nsie0M7diMjnoZTVhIaBiofqkMpZRSKlcSY+D0mTaszlI5BRYOLcR44/ULeX3Qh3hLF1hSSql60Ol/s4p70+/IiM1Dj29ndNuL2F39iIHl0pyzpYjDLVveQU4t4l93X4R85gHEHyVghPa4TqFQSik1HQwQJxp1ePTJqxjYuA/PspNg/EVH9KzUUrC9vR3IqtL3ASulVL1op2rWcItTfPhhJ0+9sQL71kex/XECjs21VgehcXPVly8/yZtvrmZfRPB+4hHEgqDj5TarmUGj0/2UUkpVm1uV9uzZFna+uB5uegq7aRSPY7HaCgJOvRuolFJl0+l/s8Yo0aiHx57Yiv3xJxF/nMWmiVut7rwOFUBvbxevH1+Ab9sriAWLaGaV1YpHJ/8ppZSaFlEcx+Hhh7fDjU9hNY/ShpfbrQUEcTh+vJto2zmkdQgAW/NIKTWD6JWqWcMhGvWDbRCPO9p3lbQX3ToS8SPNEcCdcbGA5qLbKqWUUpUzGCMkEl78gRgA18o8ms0oL7+8jkO+Yby3PY4ILCLAvAmrASqlVOPQTpVSSimlam4tPrwxw892bmd46x68i3rBwEbaWKYDfUqpGUY7VUoppZSqKRu4hCYOHV7KQGgY36JeLAPbpZMWvUKllJqBGuqeKhH5bRHZIyJREfnBBNt+XkSSIjKc9XVdbVqqlFJqrtKsqpwAFkIiYYHtlkhvF592qJRSM1ajXak6Cfw5cAvQNIntf26MuXp6mzQTGMCQSHjAG5/EtkoppSqgWVWWVE7ZCTSLlFKzTUN1qowx9wKIyGXA0jo3Z4ZwS6mfONHBE69diP/OnwHQSn5ZdCGJbUbzHtcKS0opNXmaVVPlllLv6wuy84WN+H7xXsTWTpVSanZpqOl/ZdgsImdF5JCIfEVEGqqTOP0SGBNm9541PPFhG55P7ET8cRaaADfQnbOlRRzicR577PK88UHvjH8bKKVUQ5vDWZUEwhw40MMDb/TAJx/BCkZoc3San1JqdpnJJ/bngA3AMWA98H+BBPCNQhuLyBeBLwL09CyoUROnkwOMsnv3Ot5mFO8Vr4GBTbSyWlpytrSI4SXOjgevZfjyl0F0hFAppWpk0lk1O3MqwoEDK3iltw3f9c+CgYtMiA1WM8bE6D09H2vhB/VuqFJKVaxmlyhE5BkRMUW+Xpjq/owxh40xR4wxjjFmP3AP8Isltv+eMeYyY8xlnZ3zKvlTGoR7Y+/p0x1YC84AsE5aWE1L3pZWapX68+dbsDrO1a6JSik1w9Qzq2ZfTrkDeKdPt2N19AGwQpr5mDRhoob7H97GybVv4ll3CAysIFjPxiqlVEVqdqXKGHPddP8KmNs3BwUK3EellFJq8jSrpo8H2ICHc6eDPPrSOuTmJ7EDUTyOxZXWfFrx1buJSilVtoa6mUZEPCISwF3CwhaRQLG55yJym4h0p75fC3wF+FntWltPBojR2zuPvoSDLOoFKi84ESKJcYQ39q/EXv9mFdqplFKzj2bVZBggyvnzIU6ca8ZaeZhNBAg6ws6d25GbnsJqitKGlxusLu1QKaVmvIbqVAF3AxHgLuBXUt/fDSAiPan1PXpS294IvCEiYeBh4F7gL2rf5FpLYkyY119fxSPvduP51MNY/hgdxsuSSVX2LaybBB1hP/c+uI0TG/fhWf8ORm+9UkqpQjSrSnKLUxw6tIT79qyCTz2MFRqhxdhgLBIJD+KPAXCZzMfTcB9FlFJq6hqqUIUx5mvA14o8dxwIZf38h8Af1qRhDSNOMhnjscev5Myag/guOQoG1tHCWmlByrxS5QfiH7Xz0O41eG59DNsXxxluxgqNVLX1Sik1G2hWlRLHmCjPP7+ZI/N78d74LAKsNM10ix9dn0opNVvp8NCMEuWjj7o4FbXxrDwKBq6VTi6mtewOFbhvgpde+hjWltcRX5x5MT/xnR+vWquVUkrNFTHOn2/hvROdeDceQASulPlslvn1bphSSk0r7VTNMMmkBbZbza9ZbDrxV32/PaMt4GjRC6WUUlOXTFpguXniQVhCc51bpJRS0087VUoppZSaZu60v3jcC3aCqUwDFByMgVjMh/hi09Q+pZSqTEPdU6WUUkqp2cbgYZRzZ0Ps3LUB3y/9FLEMfmPhldJju17ieOPw8JNXcP5je/Es/xBMa43arZRSk6edKqWUUkpNixAWHjPCgTdX8uq5AN47HkEsQ5vxcLl0YBW9H9jQTJTh/hD3PfcxzE1P4wmNYBnBEotkTf8KpZSamHaqZowkxsCpU+1YC04Dc3T1SKWUUg3KAQynT8/H6jpDAOEWQrz91gW8ejaE7/rnwMBqQlwooZIFloJEiQ4H+NkjV+H75Z9ieZM0Ox4useZhkyBeuz9KKaUmRTtVM0KMRCLJE09dzulV7+JddQQMXCQt9W6YUkopBcRxnBjPv7CJo/NP473+OULY2Ahnz87H6nAHA3ukmTVMnF02hoGBEAQjiDeJZeAKq40FJsHQUBNer5OzfSUVcJVSqhq0UEVDM0CE8+e9/PihKzl75Yt4Vh3BcoRfkE4uIFjvBiqllJrTDDDKyIjw0we3cfzi/Xg3vo2Fu4bieL4yP3asFR/zoxYPPXIVI77RypqslFLTQDtVDS0OJHn88atIbnsJq3WYZsfmdqubrrJKqRsutm3iUR9vHuzB3vAWoJcrlVJKlSsJJHjmmS2MbngTu7Mfn7H4uCxgAYGq/IYObC6hmZdf2sD5pccRv1YAVEo1Hu1UNTS35Gw06kUCUQC2WvPxU94aUjff/BLLznfx451bCd/4JNbCXmxH2KpriCillCqLm1ORiB+aIyBwibQRwlu13+BPTe2LRPzQFNEbipVSDUkvUsww5c0bNxgDg4MtvHKqJVN9ye/YbLVaaZvCeiFKKaVUKXp/k1JqLtIrVbOcRRyS7g29u3evx3PlbsQytOPnImseHg0/pZRSSimlKqKdqlnL4CFC+LyP948vRAQcRxDLvSrVRZOOJiqllFJKKVUF2qlqaIZEwsIxBuyprMph8DLC2d427nvmElh+fNpaqJRSai5zcBwhnrAQX24BCcHBGIhGfRCI1Kl9SilVG3pPVUNyS9QODPh5+JlLsT71EFZzFI8RWmTifzLBYAFHjy5Glp4ES++ZUkopVU0GiBKJ2Dz8xDbi1z+D3X4eMTBPvO7U80SSx57eSu/qd/Ck1ldsF1+9G66UUtNCO1UNxwFGeP/9xbxwuBvPJx7B8iQJOh6uszrLXuNDKaWUqg4DjHDiRAdPvLYaz8cfw/bH8Ts211rttBFn+HwTDz97Oc71z+BpHcZyhK3WfDrKWg5EKaUan3aqGoobVMePd/PcwR58t+9EBFaYZjZb87D0HiillFJ1N8KZM2089vMN+D57H2LBQhPgSqsdP1Hiox7ue+AavP/mp1i+OEHHk3quvOVAlFJqJtBOVUNxp+n197dhzTuPCHThY4vMr3O7lFJKqTTD+fMtSNsgYkEQm+3SAbj3UQ0Ph3B8ccQXBwO/YHVqYSSl1Kync8kanE73U0op1ci8JXLKFtEOlVJqTtBP7A3Dvel3eDjAwaPd2OveBgMe/SdSSinVENycGh318uY7y7A3vgmg6x0qpRQ6/a9BuPdSHTu2gGcOLHOLU3gTNDk266W13o1TSik157k51dvbxmMvX4zc/CR2IIrXsdhszat345RSqu60U1V3SSDC7t0Xc8BE8dz6BCKw1DSx1ZpfleIUAhiT+W7C7R3REuxKKaXS3Kq0b711AbtPB/F+cidiGTocL1dbnTqjQiml0Ol/DSBGPO5h//41eC7biwhcQhtXSHtVOlQXdg7QE2/i4UevwL7+GSQQy5lWmF5SuKUljBkKAXAgeA6Z31/x71ZKKTUbuIv67tmzDs8VexDLcCEhrrW6tEOllFIpejasO4PjpDpPqStEy2muyp5Xrz7G2tYYP3noKs5d/TyeFR8gRlgtbZkgHMbiJMJ1179K+3trSBxbimM5eG96uiptUEopNTs4jiCpxeRX0KwFKJRSKkvDdKpExC8i3xeRYyIyJCJ7ReS2CV7z+yJySkQGROQfRGSGrSpoAEM06gVfFKlaPrmhF4/7uP+5TXDHI1ihEbyOzTqZTxBvztbnsPnA53DLrS+xoW8x8Re3TmaWoFJKzTlzNatiMQ/YSbCSJbcTDKOjPsQfq1nrlFKqETRMpwr3/q4PgGuBNuArwI9FZEWhjUXkFuAu4EZgBbAS+NMatLNKxlak3/HcBvy/9FMAWrCxKx79c3tEx44tQpaeRGwH2wgXW/OKTtWIYvGe2Gy+9BDXLAwT2/nxCtuglFKz0hzKKjen+vqa+ddHLsf7ufsQXwK/sQjm3ZLt4GWE999byhNvL8H3yYcAmK+3biul5oiGOdsZY8LA17IeelBEjgBbgKMFXvJrwPeNMW8BiMifAf+EG14NLoExo+zes5a3TQzPJx5DBBaZAFdI+7RMqfCINeF+HQQDtLaGYbil6m1QSqmZbu5klVtE6cCBFbxyOoT3jkewLMM8x8s1VmfO4J8Qx0rGefb5zXyw4CO8Nz2DAD2mWSvYKqXmjEa6UpVDRLqBNcBbRTZZD+zL+nkf0C2SWta9ocUYGmrmrfeW4rl0HwJsoo1t0lGFq1TpSYUQCIzCqB+TFLyT3K8D2HYSr53AicywGSpKKVVjszerYoyOetn92kV4r3gFEcNFJsQNVte4RekNXmKcPTOfo/0h7LUHsRAulflskLayBwkdBI83gddyiEc9xHAIhiLISBNhx8FIkkBTBGdY7+1SSjWGhrlSlU1EvLgjef/bGHOwyGYhYCDr5/T3LUBfgX1+Efhi6seoz3fTm1VqbmV+rd4NyNMJnK13IxqUHpvi9NgUp8emuIvq3YBKVDurGjan/lO9G5BH/5sqTo9NcXpsitNjU9ykc6pmnSoReQZ3Dnohu4wxV6e2s4Af4tZw/e0SuxwGsucVpL8fKrSxMeZ7wPdSv2OPMeaySTd+DtFjU5wem+L02BSnx6Y4EdlT7zaMV8+s0pyaHD02xemxKU6PTXF6bIqbSk7VrFNljLluom1ERIDvA93A7caYeInN3wIXVE0AAAAGGklEQVQ2AT9O/bwJ6DXG5F2lUkoppSZDs0oppVQ5Gu2equ8CFwN3GGMiE2z7f4BfF5F1IjIfuBv4wTS3TymllNKsUkoplaNhOlUishz4L8AlwCkRGU59/YfU8z2pn3sAjDE7gb8EngaOpb7+ZJK/7ntV/wNmDz02xemxKU6PTXF6bIqbccemhlk1445NDemxKU6PTXF6bIrTY1PcpI+NGKOrvCqllFJKKaVUuRrmSpVSSimllFJKzUTaqVJKKaWUUkqpCszZTpWI+EXk+yJyTESGRGSviNxW73Y1ChH5bRHZIyJREflBvdtTTyLSLiI7RCScer/8+3q3qVHo+6QwPb+UJiL/KCIficigiBwSkS/Uu02NSN9Hpen5Z4zmVHH6PilMzy+llZNTDbn4b414gA9w1yM5DtwO/FhENhpjjtazYQ3iJPDnwC1AU53bUm9/i7sWTTfuzekPicg+Y8xb9W1WQ9D3SWF6fintG8CvG2OiIrIWeEZE9hpjXq13wxqMvo9K0/PPGM2p4vR9UpieX0qbck7N2StVxpiwMeZrxpijxhjHGPMgcATYUu+2NQJjzL3GmPuAOb2WiogEgc8BXzHGDBtjXgDuB361vi1rDPo+KUzPL6UZY94yxkTTP6a+VtWxSQ1J30el6fnHpTlVmr5PCtPzS2nl5NSc7VSNJyLdwBrchRqVSlsDJI0xh7Ie2wesr1N71Ayk55d8IvIdERkBDgIfAQ/XuUkNT99HqgjNKVUxPb/km2pOaacKEBEv8E/A/zbGHKx3e1RDCQED4x4bAFrq0BY1A+n5pTBjzG/i/nd0DXAvEC39irlN30eqBM0pVRE9vxQ21ZyatZ0qEXlGREyRrxeytrOAH+LORf7tujW4hiZ7bBQAw0DruMdagaE6tEXNMHPx/DIVxphkaqrSUuA36t2eWtOcKk5zako0p1TZ5uL5ZSqmklOztlCFMea6ibYREQG+j3tj5+3GmPh0t6sRTObYqIxDgEdELjTGvJt6bBN6eVxNYK6eX8rkYQ7eU6U5VZzm1JRoTqmyzNXzS5kmzKlZe6Vqkr4LXAzcYYyJ1LsxjUREPCISAGzAFpGAiMzaTngxxpgw7iXfe0QkKCLbgU/jjurMefo+KUnPLwWIyAIR+bciEhIRW0RuAf4d8FS929ag9H1UhJ5/XJpTpen7pCQ9vxRQbk6JMaY2LWwwIrIcOIo7PzKR9dR/Mcb8U10a1UBE5GvAn4x7+E+NMV+rfWvqS0TagX8AbsatHnSXMeaf69uqxqDvk8L0/FKciHQBP8EdSbeAY8D/Msb8f3VtWAPS91Fpev4ZozlVnL5PCtPzS3Hl5tSc7VQppZRSSimlVDXM9el/SimllFJKKVUR7VQppZRSSimlVAW0U6WUUkoppZRSFdBOlVJKKaWUUkpVQDtVSimllFJKKVUB7VQppZRSSimlVAW0U6WUUkoppZRSFdBOlVIzlIgsEpF/FpGDIpIUkR/Uu01KKaVUmuaUmku0U6XUzOUHzgLfBF6uc1uUUkqp8TSn1JyhnSqlGpSIdInIRyLy1azHPiYioyLyi8aYo8aY3zXG/ADor19LlVJKzUWaU0qN0U6VUg3KGHMG+Dxwt4hcJSJNwL8A/2KM+UldG6eUUmrO05xSaoyn3g1QShVnjHlURL4D/BPwLO5Uit+pb6uUUkopl+aUUi69UqVU4/sSEAP+I/AfjDHDdW6PUkoplU1zSs152qlSqvGtAJYBBlhZ36YopZRSeVagOaXmOO1UKdXARMSLO6XifuAPge+KSE99W6WUUkq5NKeUcuk9VUo1tj8DFgA3AQPArcAPReR6Y4wjIpektmsF0j/HjDEH6tNcpZRSc4zmlFKAGGPq3QalVAEici3wBHCzMeaZ1GMLgTeAbxtjviEihf4DPmaMWVGzhiqllJqTNKeUGqOdKqWUUkoppZSqgN5TpZRSSimllFIV0E6VUkoppZRSSlVAO1VKKaWUUkopVQHtVCmllFJKKaVUBbRTpZRSSimllFIV0E6VUkoppZRSSlVAO1VKKaWUUkopVQHtVCmllFJKKaVUBbRTpZRSSimllFIV+P8BxzjG+dl5TrYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_  # 以SVM分类器为例来演示AdaBoost的基本策略\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "m = len(X_train)\n",
    "\n",
    "plt.figure(figsize=(14, 5))\n",
    "for subplot, learning_rate in [(121, 1), (122, 0.5)]:\n",
    "    sample_weight = np.ones(m)\n",
    "    plt.subplot(subplot)\n",
    "    for i in range(5):\n",
    "        svm_clf = SVC(kernel='rbf', C=0.05, random_state=42)\n",
    "        svm_clf.fit(X_train, y_train, sample_weight=sample_weight)\n",
    "        y_pred = svm_clf.predict(X_test)\n",
    "        sample_weight[y_pred != y_train] *= (1 + learning_rate)\n",
    "        plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n",
    "        plt.title('learn_rate = {}'.format(learning_rate))\n",
    "    if subplot == 121:\n",
    "        plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
    "        plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
    "        plt.text(-0.5, 0.10, \"3\", fontsize=14)\n",
    "        plt.text(-0.4, 0.55, \"4\", fontsize=14)\n",
    "        plt.text(-0.3, 0.90, \"5\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada_clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),\n",
    "                             n_estimators=200,\n",
    "                             learning_rate=0.6,\n",
    "                             random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada_clf.fit(X_train, y_train)\n",
    "plot_decision_boundary(ada_clf, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X = np.random.rand(100, 1) - 0.5\n",
    "y = 3 * X[:, 0]**2 + 0.05 * np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2)\n",
    "tree_reg1.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = y - tree_reg1.predict(X)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=2)\n",
    "tree_reg2.fit(X, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3 = y2 - tree_reg2.predict(X)\n",
    "tree_reg3 = DecisionTreeRegressor(max_depth=2)\n",
    "tree_reg3.fit(X, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array([[0.8]])\n",
    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75026781])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(regressors,\n",
    "                     X,\n",
    "                     y,\n",
    "                     axes,\n",
    "                     label=None,\n",
    "                     style='r-',\n",
    "                     data_style='b.',\n",
    "                     data_label=None):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
    "    y_pred = sum(\n",
    "        regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
    "    if label or data_label:\n",
    "        plt.legend(loc='upper center', fontsize=16)\n",
    "    plt.axis(axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$y$')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 11))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_predictions([tree_reg1],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label='$h_1(x_1)$',\n",
    "                 style='g-',\n",
    "                 data_label='Training set')\n",
    "plt.ylabel('$y$', fontsize=16, rotation=0)\n",
    "plt.title('Residuals and tree predictions', fontsize=16)\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_predictions([tree_reg1],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label='$h_1(x_1) = h_1(x_1)$',\n",
    "                 data_label='Training set')\n",
    "plt.ylabel('$y$', fontsize=16, rotation=0)\n",
    "plt.title('Ensemble predictions', fontsize=16)\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_predictions([tree_reg2],\n",
    "                 X,\n",
    "                 y2,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label=\"$h_2(x_1)$\",\n",
    "                 style=\"g-\",\n",
    "                 data_style=\"k+\",\n",
    "                 data_label=\"Residuals\")\n",
    "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_predictions([tree_reg1, tree_reg2],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "plt.subplot(325)\n",
    "plot_predictions([tree_reg3],\n",
    "                 X,\n",
    "                 y3,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label=\"$h_3(x_1)$\",\n",
    "                 style=\"g-\",\n",
    "                 data_style=\"k+\")\n",
    "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_predictions([tree_reg1, tree_reg2, tree_reg3],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.1, 0.8],\n",
    "                 label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=1.0, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=3, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbrt = GradientBoostingRegressor(max_depth=2,\n",
    "                                 n_estimators=3,\n",
    "                                 learning_rate=1.0,\n",
    "                                 random_state=42)\n",
    "gbrt.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=3, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_slow_1 = GradientBoostingRegressor(max_depth=2,\n",
    "                                        n_estimators=3,\n",
    "                                        learning_rate=0.1,\n",
    "                                        random_state=42)\n",
    "gbrt_slow_1.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=200, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_slow_2 = GradientBoostingRegressor(max_depth=2,\n",
    "                                        n_estimators=200,\n",
    "                                        learning_rate=0.1,\n",
    "                                        random_state=42)\n",
    "gbrt_slow_2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'learning_rate = 0.1, n_estimators = 3')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions([gbrt],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label='Ensemble predictions')\n",
    "plt.title('learning_rate = {}, n_estimators = {}'.format(\n",
    "    gbrt.learning_rate, gbrt.n_estimators))\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_slow_1],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label='Ensemble predictions')\n",
    "plt.title('learning_rate = {}, n_estimators = {}'.format(\n",
    "    gbrt_slow_1.learning_rate, gbrt_slow_1.n_estimators))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'learning_rate=0.1,n_estimators=3')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions([gbrt_slow_2],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label='Ensemble predictions')\n",
    "plt.title('learning_rate={},n_estimators={}'.format(gbrt_slow_2.learning_rate,\n",
    "                                                    gbrt_slow_2.n_estimators))\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_slow_1],\n",
    "                 X,\n",
    "                 y,\n",
    "                 axes=[-0.5, 0.5, -0.5, 1],\n",
    "                 label='Ensemble predictions')\n",
    "plt.title('learning_rate={},n_estimators={}'.format(gbrt_slow_1.learning_rate,\n",
    "                                                    gbrt_slow_1.n_estimators))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 提前停止策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=42)\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=150, random_state=42)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "errors = [\n",
    "    mean_squared_error(y_val, y_pred) for y_pred in gbrt.staged_predict(X_val)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.05610899559053935,\n",
       " 0.047560923813483064,\n",
       " 0.04124088566823302,\n",
       " 0.03517911550570251,\n",
       " 0.030576406839652973,\n",
       " 0.026637609691987457,\n",
       " 0.023613435122730796,\n",
       " 0.020369427024608137,\n",
       " 0.018277048359280665,\n",
       " 0.016091796347069243,\n",
       " 0.014585571409641585,\n",
       " 0.01314784747126986,\n",
       " 0.01184057668005128,\n",
       " 0.010756151385176995,\n",
       " 0.009974274034504127,\n",
       " 0.009207844894461165,\n",
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       " 0.007506170396302726,\n",
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       " 0.006572858688497687,\n",
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       " 0.004476259729372586,\n",
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       " 0.003814385846946538,\n",
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       " 0.0036747256319290484,\n",
       " 0.0036377893287685686,\n",
       " 0.0035722601062841775,\n",
       " 0.003539916282594281,\n",
       " 0.0035273286851342117,\n",
       " 0.0034752693583887497,\n",
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       " 0.0033698104419812693,\n",
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       " 0.00331414931160449,\n",
       " 0.0033033879441172554,\n",
       " 0.003298304164932583,\n",
       " 0.0032922818129117004,\n",
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       " 0.003282420990392003,\n",
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       " 0.0032441393456282396,\n",
       " 0.003220212739269214,\n",
       " 0.003215288185284655,\n",
       " 0.003212239539232774,\n",
       " 0.0032038937254658274,\n",
       " 0.0031737262194907797,\n",
       " 0.003169935755046025,\n",
       " 0.003153771397206335,\n",
       " 0.0031520997891681733,\n",
       " 0.0031449824167012323,\n",
       " 0.003149296446884412,\n",
       " 0.0031424475607028063,\n",
       " 0.0031413402187646174,\n",
       " 0.00313877315128887,\n",
       " 0.0031333319139971567,\n",
       " 0.003127106291683553,\n",
       " 0.003123763358699128,\n",
       " 0.0031259535257383577,\n",
       " 0.0031104157041700314,\n",
       " 0.0031034608364714066,\n",
       " 0.003098225347050626,\n",
       " 0.0031001664072027152,\n",
       " 0.003085146529801172,\n",
       " 0.0030710416561175215,\n",
       " 0.0030698970525524305,\n",
       " 0.0030825823757418104,\n",
       " 0.0030721448933086837,\n",
       " 0.0030710552895814943,\n",
       " 0.003053891602464799,\n",
       " 0.003044547547476283,\n",
       " 0.00304354893415663,\n",
       " 0.0030429036813461275,\n",
       " 0.0030420047968681405,\n",
       " 0.003033182523232854,\n",
       " 0.0030293337886219108,\n",
       " 0.003027333448428982,\n",
       " 0.0030330838545840687,\n",
       " 0.0030350737612532285,\n",
       " 0.003026341437906243,\n",
       " 0.0030316221080234755,\n",
       " 0.0030297223717657986,\n",
       " 0.0030260588905879284,\n",
       " 0.003023701609089896,\n",
       " 0.0030315844877907617,\n",
       " 0.0030347374508727736,\n",
       " 0.0030392027704575324,\n",
       " 0.003038356136788138,\n",
       " 0.003041070343036983,\n",
       " 0.003039237382688167,\n",
       " 0.0030357729678834916,\n",
       " 0.0030389992433268505,\n",
       " 0.0030242165411718223,\n",
       " 0.00301663684402473,\n",
       " 0.003018065348805215,\n",
       " 0.003019197969804213,\n",
       " 0.0030170825334108024,\n",
       " 0.0030207699828886585,\n",
       " 0.003019175955706901,\n",
       " 0.003016136607419031,\n",
       " 0.003012602692475849,\n",
       " 0.003009529324545136,\n",
       " 0.0030116263431501887,\n",
       " 0.003010263265992808,\n",
       " 0.003018632136738277,\n",
       " 0.003020887438941288,\n",
       " 0.0030240587246706052,\n",
       " 0.003021299439099846,\n",
       " 0.003022171732436713,\n",
       " 0.0030239529916090114,\n",
       " 0.0030218404288224437,\n",
       " 0.0030187072363728473,\n",
       " 0.0030197487152145565,\n",
       " 0.0030132596188507045,\n",
       " 0.0030141784385354876,\n",
       " 0.0030159502286101576,\n",
       " 0.003020575426043514,\n",
       " 0.00301929619995512,\n",
       " 0.0030223332254702513,\n",
       " 0.0030198446406829245,\n",
       " 0.0030180207878347017,\n",
       " 0.003017731775675953,\n",
       " 0.0030193682639834247,\n",
       " 0.00302647508696835,\n",
       " 0.0030224070120508316,\n",
       " 0.003022180646034352,\n",
       " 0.003020745128039215,\n",
       " 0.0030205535632122373,\n",
       " 0.003021865829188429,\n",
       " 0.003027996500141162,\n",
       " 0.003029537203283488,\n",
       " 0.0030322881100434003,\n",
       " 0.003031055012662433,\n",
       " 0.0030282113406660967]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bst_n_estimators = np.argmin(errors) + 1\n",
    "bst_n_estimators  # 第117个索引为最小值，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=118, presort='auto', random_state=42,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_best = GradientBoostingRegressor(max_depth=2,\n",
    "                                      n_estimators=bst_n_estimators,\n",
    "                                      random_state=42)\n",
    "gbrt_best.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003009529324545136"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_error = np.min(errors)\n",
    "min_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Best Model (118 trees)')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(errors, 'b.-')\n",
    "plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], 'k--')\n",
    "plt.plot([0, 120], [min_error, min_error], 'k--')\n",
    "plt.axis([0, 150, 0.0025, 0.0035])\n",
    "plt.title('Val Error')\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.5, 1])\n",
    "plt.title('Best Model (%d trees)' % bst_n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提前停止策略\n",
    "gbrt = GradientBoostingRegressor(max_depth=2, random_state=42,\n",
    "                                 warm_start=True)  # 缓存之前树的计算结果\n",
    "error_goingup = 0\n",
    "min_val_error = float('inf')\n",
    "\n",
    "for n_estimator in range(1, 151):\n",
    "    gbrt.n_esttimators = n_estimator\n",
    "    gbrt.fit(X_train, y_train)\n",
    "    y_pred = gbrt.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "    if val_error < min_val_error:\n",
    "        min_val_error = val_error\n",
    "        error_goingup = 0\n",
    "    else:\n",
    "        error_goingup += 1\n",
    "        if error_goingup == 5:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "print(gbrt.n_estimators)  # 连续5次错误提升才退出，但不代表最低错误值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking（堆叠集成）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/6.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "\n",
    "X_train_val, X_test, y_train_val, y_test = train_test_split(mnist.data,\n",
    "                                                            mnist.target,\n",
    "                                                            test_size=10000,\n",
    "                                                            random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train_val,\n",
    "                                                  y_train_val,\n",
    "                                                  test_size=10000,\n",
    "                                                  random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "rf_clf = RandomForestClassifier(random_state=42)\n",
    "et_clf = ExtraTreesClassifier(random_state=42)\n",
    "svm_clf = LinearSVC(random_state=42)\n",
    "mlp_clf = MLPClassifier(random_state=42)\n",
    "\n",
    "estimators = [rf_clf, et_clf, svm_clf, mlp_clf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
      "            oob_score=False, random_state=42, verbose=0, warm_start=False)\n",
      "Training the ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
      "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
      "           oob_score=False, random_state=42, verbose=0, warm_start=False)\n",
      "Training the LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
      "     verbose=0)\n",
      "Training the MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
      "       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
      "       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "for estimator in estimators:\n",
    "    print('Training the', estimator)\n",
    "    estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2., 2., 2.],\n",
       "       [7., 7., 7., 7.],\n",
       "       [4., 4., 4., 4.],\n",
       "       ...,\n",
       "       [4., 4., 4., 4.],\n",
       "       [9., 9., 9., 9.],\n",
       "       [4., 4., 4., 4.]], dtype=float32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val_predictions = np.empty((len(X_val), len(estimators)), dtype=np.float32)\n",
    "for index, estimator in enumerate(estimators):\n",
    "    X_val_predictions[:, index] = estimator.predict(X_val)\n",
    "\n",
    "X_val_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=-1,\n",
       "            oob_score=True, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender = RandomForestClassifier(n_estimators=200,\n",
    "                                            oob_score=True,\n",
    "                                            random_state=42,\n",
    "                                            n_jobs=-1)\n",
    "rnd_forest_blender.fit(X_val_predictions, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9614"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest_blender.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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